{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "cb19f71d-51c8-417f-829e-3179d3319dcd",
    "_uuid": "c3b9226e142667d6b96e34daf7d6e42bea0ea1e2"
   },
   "source": [
    "# **通过Logistic Regression预测Titanic乘客是否能在事故中生还**\n",
    "<font color='red'>请搜索\"TODO\"字样, 在有\"TODO\"字样的地方添加代码完成作业</font>\n",
    "\n",
    "1. [导入工具库和数据](#t1.)\n",
    "2. [查看缺失数据](#t2.)\n",
    "    * 2.1. [年龄](#t2.1.)\n",
    "    * 2.2. [仓位](#t2.2.)\n",
    "    * 2.3. [登船地点](#t2.3.)\n",
    "    * 2.4. [对数据进行调整](#t2.4.)\n",
    "        * 2.4.1 [额外的变量](#t2.4.1.)\n",
    "3. [数据分析](#t3.)\n",
    "4. [Logistic Regression](#t4.)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "33c91cae-2ff8-45a6-b8cb-671619e9c933",
    "_uuid": "0a395fd25f20834b070ef55cb8987c8c1f9b55f9"
   },
   "source": [
    "<a id=\"t1.\"></a>\n",
    "# 1. 导入工具库和数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "de05512e-6991-44df-9599-da92a7e459ac",
    "_uuid": "d8bdd5f0320e244e4702ed8ec1c2482b022c51cd"
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "\n",
    "from sklearn import preprocessing\n",
    "import matplotlib.pyplot as plt \n",
    "plt.rc(\"font\", size=14)\n",
    "import seaborn as sns\n",
    "sns.set(style=\"white\") #设置seaborn画图的背景为白色\n",
    "sns.set(style=\"whitegrid\", color_codes=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "e0a17223-f682-45fc-89a5-667af9782bbe",
    "_uuid": "7964157913fbcff581fc1929eed487708e81ac9c"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>cabin</th>\n",
       "      <th>embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>B5</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>C22 C26</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "0     1.0       1.0                    Allen, Miss. Elisabeth Walton  female   \n",
       "1     1.0       1.0                   Allison, Master. Hudson Trevor    male   \n",
       "2     1.0       0.0                     Allison, Miss. Helen Loraine  female   \n",
       "3     1.0       0.0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4     1.0       0.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "       age  sibsp  parch  ticket      fare    cabin embarked  \n",
       "0  29.0000    0.0    0.0   24160  211.3375       B5        S  \n",
       "1   0.9167    1.0    2.0  113781  151.5500  C22 C26        S  \n",
       "2   2.0000    1.0    2.0  113781  151.5500  C22 C26        S  \n",
       "3  30.0000    1.0    2.0  113781  151.5500  C22 C26        S  \n",
       "4  25.0000    1.0    2.0  113781  151.5500  C22 C26        S  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 将数据读入 DataFrame\n",
    "df = pd.read_csv(\"./titanic_data.csv\")\n",
    "\n",
    "# 预览数据\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "872d0de9-a873-4b60-b1ee-d557ee39d8a1",
    "_uuid": "d38222a64d4dfd1d1ee1a7ee1f58c4aa54560de3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据集包含的数据个数 1310.\n"
     ]
    }
   ],
   "source": [
    "print('数据集包含的数据个数 {}.'.format(df.shape[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6578c0da-7bcf-433d-9f28-a66d8dfa6fa3",
    "_uuid": "8660e63a62c2fcdb4f7633380166438caf5edae9"
   },
   "source": [
    "<a id=\"t2.\"></a>\n",
    "# 2. 查看缺失数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "_cell_guid": "29dddd33-d995-4b0f-92ea-a361b368cc42",
    "_uuid": "d4fe22ead7e187724ca6f3ba7ba0e6412ae0e874"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass         1\n",
       "survived       1\n",
       "name           1\n",
       "sex            1\n",
       "age          264\n",
       "sibsp          1\n",
       "parch          1\n",
       "ticket         1\n",
       "fare           2\n",
       "cabin       1015\n",
       "embarked       3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查看数据集中各个特征缺失的情况\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "7776faeb-6a8f-4460-a367-4b087d2cc089",
    "_uuid": "696b428bd3ca49421f650665267ce7ca1b358814"
   },
   "source": [
    "<a id=\"t2.1.\"></a>\n",
    "## 2.1.    年龄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "_cell_guid": "d4ee6559-6d0c-409d-9dca-1d105a4ccd8a",
    "_uuid": "129cf984d05d9ce97c54548145e65f9e4b9b0c37"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"age\" 缺失的百分比  20.15%\n"
     ]
    }
   ],
   "source": [
    "# \"age\" 缺失的百分比 \n",
    "print('\"age\" 缺失的百分比  %.2f%%' %((df['age'].isnull().sum()/df.shape[0])*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "951f7bb8-779c-4eac-85a2-3fdcfdcd293e",
    "_uuid": "c8fff460fb532a063f6944450809014ca831ca52"
   },
   "source": [
    "约 20% 的乘客的年龄缺失了. 看一看年龄的分别情况."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "_cell_guid": "6d65fcfa-52bf-45ab-b959-64a32c1c1976",
    "_uuid": "c6fd60f15d5e803d4dffc89e782c6fbc72445a83"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df[\"age\"].hist(bins=15, color='teal', alpha=0.6)\n",
    "ax.set(xlabel='age')\n",
    "plt.xlim(-10,85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e62d6951-d968-43ba-aabf-add90524d042",
    "_uuid": "24c201948b9c8c8076ab01271a4790d9db9096b5"
   },
   "source": [
    "由于“年龄”的偏度不为0, 使用均值替代缺失值不是最佳选择, 这里可以选择使用中间值替代缺失值\n",
    "\n",
    "\n",
    "<font color=red> 注: 在概率论和统计学中，偏度衡量实数随机变量概率分布的不对称性。偏度的值可以为正，可以为负或者甚至是无法定义。在数量上，偏度为负（负偏态）就意味着在概率密度函数左侧的尾部比右侧的长，绝大多数的值（不一定包括中位数在内）位于平均值的右侧。偏度为正（正偏态）就意味着在概率密度函数右侧的尾部比左侧的长，绝大多数的值（不一定包括中位数）位于平均值的左侧。偏度为零就表示数值相对均匀地分布在平均值的两侧，但不一定意味着其为对称分布。</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "_cell_guid": "1d70c27b-1e4d-4d5e-8a39-c134389d436c",
    "_uuid": "4f13840d4f9bf1b4331523c99274aa0627485e6c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The mean of \"Age\" is 29.88\n",
      "The median of \"Age\" is 28.00\n"
     ]
    }
   ],
   "source": [
    "# 年龄的均值\n",
    "print('The mean of \"Age\" is %.2f' %(df[\"age\"].mean(skipna=True)))\n",
    "# 年龄的中间值\n",
    "print('The median of \"Age\" is %.2f' %(df[\"age\"].median(skipna=True)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "dea7b01c-c8c1-401f-a336-36ee73de2222",
    "_uuid": "e1a08114e302ddc90266e5f065b3f0b5a200bc89"
   },
   "source": [
    "<a id=\"t2.2.\"></a>\n",
    "## 2.2. 仓位"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "_cell_guid": "1a1ad808-0a63-43ac-b757-71195880ed4f",
    "_uuid": "1acbce9c6bc5d586dda3e47b7506067a85524e66"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"Cabin\" 缺失的百分比 77.48%\n"
     ]
    }
   ],
   "source": [
    "# 仓位缺失的百分比\n",
    "print('\"Cabin\" 缺失的百分比 %.2f%%' %((df['cabin'].isnull().sum()/df.shape[0])*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "eda8c434-63ff-4875-8566-2e194c0d3f66",
    "_uuid": "b6e037c7ac5ec476516031a06b042d8b9999ba44"
   },
   "source": [
    "约 77% 的乘客的仓位都是缺失的, 最佳的选择是不使用这个特征的值."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0e696cff-ca80-4cb5-862c-ee80f4b1ab1f",
    "_uuid": "d575319b1f528c7a153d8ab680282048cb163b14"
   },
   "source": [
    "<a id=\"t2.3.\"></a>\n",
    "## 2.3. 登船地点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "_cell_guid": "f21c2b55-2126-439d-8b1d-e96dafc97d81",
    "_uuid": "92ab9e62fb62f2a0fb9972baf6ada444187540e6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\"Embarked\" 缺失的百分比 0.23%\n"
     ]
    }
   ],
   "source": [
    "# 登船地点的缺失率\n",
    "print('\"Embarked\" 缺失的百分比 %.2f%%' %((df['embarked'].isnull().sum()/df.shape[0])*100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "d03a4187-c527-4f71-8260-0495f4523e9e",
    "_uuid": "dc97b80524057522f024d0ae6f1abe77cb994903"
   },
   "source": [
    "只有 0.23% 的乘客的登船地点数据缺失, 可以使用众数替代缺失的值."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "_cell_guid": "22924bc4-5dfa-4df7-b0d0-de3ede9c58b7",
    "_uuid": "f2a915f45264f8a580de6cc382d96b370eb75730"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "按照登船地点分组 (C = Cherbourg, Q = Queenstown, S = Southampton):\n",
      "S    914\n",
      "C    270\n",
      "Q    123\n",
      "Name: embarked, dtype: int64\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('按照登船地点分组 (C = Cherbourg, Q = Queenstown, S = Southampton):')\n",
    "print(df['embarked'].value_counts())\n",
    "sns.countplot(x='embarked', data=df, palette='Set2')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "_cell_guid": "def67427-3257-4dce-872e-7f5b4202d18a",
    "_uuid": "c57a9f8a54efa382bc94b695c9664330d01709ea"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "乘客登船地点的众数为 S.\n"
     ]
    }
   ],
   "source": [
    "print('乘客登船地点的众数为 %s.' %df['embarked'].value_counts().idxmax())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c4c55f99-ce99-44f9-b7a8-d4d623ae9295",
    "_uuid": "19cfaae8c484dcb1d00f69b2771e86dc249e9793"
   },
   "source": [
    "由于大多数人是在南安普顿(Southhampton)登船, 可以使用“S”替代缺失的数据值"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "684c308f-25ae-4039-9332-ddb58953a054",
    "_uuid": "3609e785d210d5a8110f7ce550e61007d066449b"
   },
   "source": [
    "<a id=\"t2.4.\"></a>\n",
    "## 2.4. 根据缺失数据情况调整数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b3025cdc-fe9f-43b6-bda1-e45c1f25e77c",
    "_uuid": "06d2762ccec3f11564870fe941fc9ac45d71662f"
   },
   "source": [
    "基于以上分析, 我们进行如下调整:\n",
    "* 如果一条数据的 \"Age\" 缺失, 使用年龄的中位数 28 替代.\n",
    "* 如果一条数据的 \"Embarked\" 缺失, 使用登船地点的众数 “S” 替代.\n",
    "* 由于太多乘客的 “Cabin” 数据缺失, 从所有数据中丢弃这个特征的值."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "_cell_guid": "bc0d7121-1008-4890-9043-07eba1524e15",
    "_uuid": "feeed4b6775f88edf5de12b0ee6ee73c16eba61d"
   },
   "outputs": [],
   "source": [
    "data = df.copy()\n",
    "data[\"age\"].fillna(df[\"age\"].median(skipna=True), inplace=True)\n",
    "data[\"embarked\"].fillna(df['embarked'].value_counts().idxmax(), inplace=True)\n",
    "data.drop('cabin', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "_cell_guid": "0cfe1c08-71a6-493e-803d-db255af01697",
    "_uuid": "d6be29651bb903964e02d3a7bcc7033513eb76c9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass      1\n",
       "survived    1\n",
       "name        1\n",
       "sex         1\n",
       "age         0\n",
       "sibsp       1\n",
       "parch       1\n",
       "ticket      1\n",
       "fare        2\n",
       "embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 确认数据是否还包含缺失数据\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 按照以上处理的方式, 处理仍然存在缺失数据的情况\n",
    "TODO:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1225</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Storey, Mr. Thomas</td>\n",
       "      <td>male</td>\n",
       "      <td>60.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3701</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1309</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pclass  survived                name   sex   age  sibsp  parch ticket  \\\n",
       "1225     3.0       0.0  Storey, Mr. Thomas  male  60.5    0.0    0.0   3701   \n",
       "1309     NaN       NaN                 NaN   NaN  28.0    NaN    NaN    NaN   \n",
       "1309     NaN       NaN                 NaN   NaN  28.0    NaN    NaN    NaN   \n",
       "1309     NaN       NaN                 NaN   NaN  28.0    NaN    NaN    NaN   \n",
       "1309     NaN       NaN                 NaN   NaN  28.0    NaN    NaN    NaN   \n",
       "1309     NaN       NaN                 NaN   NaN  28.0    NaN    NaN    NaN   \n",
       "1309     NaN       NaN                 NaN   NaN  28.0    NaN    NaN    NaN   \n",
       "1309     NaN       NaN                 NaN   NaN  28.0    NaN    NaN    NaN   \n",
       "1309     NaN       NaN                 NaN   NaN  28.0    NaN    NaN    NaN   \n",
       "\n",
       "      fare embarked  \n",
       "1225   NaN        S  \n",
       "1309   NaN        S  \n",
       "1309   NaN        S  \n",
       "1309   NaN        S  \n",
       "1309   NaN        S  \n",
       "1309   NaN        S  \n",
       "1309   NaN        S  \n",
       "1309   NaN        S  \n",
       "1309   NaN        S  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[data.isnull().values==True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1225</th>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Storey, Mr. Thomas</td>\n",
       "      <td>male</td>\n",
       "      <td>60.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3701</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      pclass  survived                name   sex   age  sibsp  parch ticket  \\\n",
       "1225     3.0       0.0  Storey, Mr. Thomas  male  60.5    0.0    0.0   3701   \n",
       "\n",
       "      fare embarked  \n",
       "1225   NaN        S  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop([1309],inplace=True)\n",
    "data[data.isnull().values==True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pclass      0\n",
       "survived    0\n",
       "name        0\n",
       "sex         0\n",
       "age         0\n",
       "sibsp       0\n",
       "parch       0\n",
       "ticket      0\n",
       "fare        0\n",
       "embarked    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"fare\"].fillna(df['fare'].value_counts().idxmax(), inplace=True)\n",
    "#data[\"fare\"].fillna(df[\"fare\"].median(skipna=True), inplace=True)\n",
    "# 确认数据是否还包含缺失数据\n",
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_cell_guid": "10dcfe1b-34f1-4bd8-b937-5ae8daf4a378",
    "_uuid": "3ee37b1151416aeeec8ebd7b94bb0184aabc57cd"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>name</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>ticket</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allen, Miss. Elisabeth Walton</td>\n",
       "      <td>female</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24160</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>Allison, Master. Hudson Trevor</td>\n",
       "      <td>male</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Miss. Helen Loraine</td>\n",
       "      <td>female</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mr. Hudson Joshua Creighton</td>\n",
       "      <td>male</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Allison, Mrs. Hudson J C (Bessie Waldo Daniels)</td>\n",
       "      <td>female</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>113781</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived                                             name     sex  \\\n",
       "0     1.0       1.0                    Allen, Miss. Elisabeth Walton  female   \n",
       "1     1.0       1.0                   Allison, Master. Hudson Trevor    male   \n",
       "2     1.0       0.0                     Allison, Miss. Helen Loraine  female   \n",
       "3     1.0       0.0             Allison, Mr. Hudson Joshua Creighton    male   \n",
       "4     1.0       0.0  Allison, Mrs. Hudson J C (Bessie Waldo Daniels)  female   \n",
       "\n",
       "       age  sibsp  parch  ticket      fare embarked  \n",
       "0  29.0000    0.0    0.0   24160  211.3375        S  \n",
       "1   0.9167    1.0    2.0  113781  151.5500        S  \n",
       "2   2.0000    1.0    2.0  113781  151.5500        S  \n",
       "3  30.0000    1.0    2.0  113781  151.5500        S  \n",
       "4  25.0000    1.0    2.0  113781  151.5500        S  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 预览调整过的数据\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 查看年龄在调整前后的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_cell_guid": "dda26046-b93b-49ee-a52e-35355ecb425c",
    "_uuid": "293aec20df86ef529d10ae1f051dfe921ba07b88"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda2/envs/py3/lib/python3.7/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n",
      "/root/anaconda2/envs/py3/lib/python3.7/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "ax = df[\"age\"].hist(bins=15, normed=True, stacked=True, color='teal', alpha=0.6)\n",
    "df[\"age\"].plot(kind='density', color='teal')\n",
    "ax = data[\"age\"].hist(bins=15, normed=True, stacked=True, color='orange', alpha=0.5)\n",
    "data[\"age\"].plot(kind='density', color='orange')\n",
    "ax.legend(['Raw Age', 'Adjusted Age'])\n",
    "ax.set(xlabel='Age')\n",
    "plt.xlim(-10,85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "6925fcc2-977b-4369-85e1-77a9210326a7",
    "_uuid": "d8280757e6bc627821fb0540c87ccd6ca110f1e0"
   },
   "source": [
    "<a id=\"t2.4.1.\"></a>\n",
    "## 2.4.1. 其它特征的处理"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "5cf98f33-fdd5-4a16-b6bf-fa36bc8b84e0",
    "_uuid": "3bfdee842f11d27ca490f466c45ef9bf3673e7ae"
   },
   "source": [
    "数据中的两个特征 “sibsp” (一同登船的兄弟姐妹或者配偶数量)与“parch”(一同登船的父母或子女数量)都是代表是否有同伴同行. 为了预防这两个特征具有多重共线性, 我们可以将这两个变量转为一个变量 “TravelAlone” (是否独自一人成行)\n",
    "\n",
    "\n",
    "<font color='red'>注: 多重共线性(multicollinearity)是指多变量线性回归中，变量之间由于存在高度相关关系而使回归估计不准确。比如虚拟变量陷阱（英语：Dummy variable trap）即有可能触发多重共线性问题。</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_cell_guid": "759c3c8e-8db6-41d9-a1a2-058a15b338a6",
    "_uuid": "d1f5815ba663f7e8cc17d7efcff73653af5b1bdb"
   },
   "outputs": [],
   "source": [
    "## 创建一个新的变量'TravelAlone'记录是否独自成行, 丢弃“sibsp” (一同登船的兄弟姐妹或者配偶数量)与“parch”(一同登船的父母或子女数量)\n",
    "data['TravelAlone']=np.where((data[\"sibsp\"]+data[\"parch\"])>0, 0, 1)\n",
    "data.drop('sibsp', axis=1, inplace=True)\n",
    "data.drop('parch', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e4a22367-b719-4204-952f-d2e9a3b8075e",
    "_uuid": "ca53796bf788bd3b015f1a79a97e050bafa2c770"
   },
   "source": [
    "对类别变量(categorical variables)使用独热编码(One-Hot Encoding), 将字符串类别转换为数值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_cell_guid": "f95361e8-2533-4731-a7ab-a99cf686ed50",
    "_uuid": "4494fcbf9faa90151e20042f74d73395fac3cc8e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>pclass</th>\n",
       "      <th>survived</th>\n",
       "      <th>age</th>\n",
       "      <th>fare</th>\n",
       "      <th>TravelAlone</th>\n",
       "      <th>embarked_C</th>\n",
       "      <th>embarked_Q</th>\n",
       "      <th>embarked_S</th>\n",
       "      <th>sex_female</th>\n",
       "      <th>sex_male</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>29.0000</td>\n",
       "      <td>211.3375</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.9167</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.0000</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>30.0000</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>25.0000</td>\n",
       "      <td>151.5500</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   pclass  survived      age      fare  TravelAlone  embarked_C  embarked_Q  \\\n",
       "0     1.0       1.0  29.0000  211.3375            1           0           0   \n",
       "1     1.0       1.0   0.9167  151.5500            0           0           0   \n",
       "2     1.0       0.0   2.0000  151.5500            0           0           0   \n",
       "3     1.0       0.0  30.0000  151.5500            0           0           0   \n",
       "4     1.0       0.0  25.0000  151.5500            0           0           0   \n",
       "\n",
       "   embarked_S  sex_female  sex_male  \n",
       "0           1           1         0  \n",
       "1           1           0         1  \n",
       "2           1           1         0  \n",
       "3           1           0         1  \n",
       "4           1           1         0  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 对 Embarked\",\"Sex\"进行独热编码, 丢弃 'name', 'ticket'\n",
    "final =pd.get_dummies(data, columns=[\"embarked\",\"sex\"])\n",
    "final.drop('name', axis=1, inplace=True)\n",
    "final.drop('ticket', axis=1, inplace=True)\n",
    "\n",
    "final.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "1430d510-1c8d-4544-8009-3911fff7afbb",
    "_uuid": "4e26c19bf719b7086addc0e1981c00836a19f189"
   },
   "source": [
    "<a id=\"t3.\"></a>\n",
    "# 3. 数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "2655428b-d69d-4c0f-85ff-e31ada8e37b9",
    "_uuid": "32e9c04a3281fb1aa8c77e1406c56cd820459202"
   },
   "source": [
    "<a id=\"t3.1.\"></a>\n",
    "## 3.1. 年龄"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_cell_guid": "9f9ca9e5-50a0-4487-ba53-815dda90af1c",
    "_uuid": "790e8d7ca89d19e276b3398e299c42893a796b79"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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RiNuIKkPmP0gP1+VEv3KvIWoxvIOouAJEBReeBbYYY15RkTC2lKji3iaiqqT/bq0dKEkBwBjz+ni7G6y1W3L++TnRuV1qo6p/lxMlNpuB/UTl/rPTG1xLNKbtN/Hn9Wei6oiDstbuJEo4PkH0MPtJ4N22j8qL/dgfH+tRY0xnfOyV8f6Iz8F/AU8TzUdXaEJxB1FLyk8H6sZlrd1M9OD8Bl5+gB2SAs7vUPwH8ATR+32GqHtd7kTZW4iSk01ED7qX2pfn57uGaN6+rUSt1wM9CH8E+EL8eX+W6MeK7Ps5SNTS9Uj8vXl93vsd6Wee65tEFSp3EH32BbcOxuf974k+681E56VjkM0+Gb/nXUT3gGXAG7Itpdbau4kKR/0kvresJKqwmy3m8g6isYlbgBeIuk/DwN+h24i6HG4kavnP/pCR1ee9zEaVJC8hav3aTfR9vih+LUVUROai+LXzgJ8N8t77ZKMxyJ8B/ofoPLbTe+w0RF3DlxEld78knkJniN/PzxMVeNkb7yM/3v7u1VnDukeKyOhywrDg3jwiIiVjolL5H+5nDEtNi7uu7QGOsNa+VO54ak2pzq/JmVakWPsUGYyJpl44YphdnEVkjNGYPREpu3hsz0eIyuWPCcaY04nKxDtEc3o9w8vFF2SEdH5FRETUjVNEyiweE7idqKvbcMdoVaMzibo/bSKa7+tv8ir5ycjo/IqIyJinbpwiIiIiIiI1qOK7cS5btqwB+CuiQcr9VfcTERERERGpVR4wE3h88eLFBRccq/hkjyjRe6jcQYiIiIiIiJTZm4iqjBekGpK9zQALFiygvr7PKbFkhFauXMmiRYvKHYbIsOj6lWqna1iqma5fqWbVdP2mUilWrVoFcW5UqGpI9nyA+vp6Ghoayh1LzdK5lWqm61eqna5hqWa6fqWaVeH1O6RhbarGKSIiIiIiUoOU7ImIiIiIiNQgJXsiIiIiIiI1qBrG7ImIiIiISIUIgoAdO3awZ88efL96Z0ZLJBI899xz5Q6jF8/zaG5upq2tDdcdebuckj0RERERESlYR0cHjuNw6KGHUldXh+M45Q5pWDo7O2lsbCx3GD3CMCSdTrN161Y6OjqYN2/eiPepbpwiIiIiIlKwzs5OZs+eTX19fdUmepXIcRzq6+uZPXs2nZ2dRdmnkj0RERERERmSYnQxlL4V89zqUxIREREREalBSvZERERERKSq/epXv+Kss87izDPP5NRTT+UTn/hEUfd/5pln0t3dXbT9XX/99XzlK18p2v76owItIiIiIiJStbZt28bnP/957r77bmbOnEkYhjz//PND2kcmkyGR6D81uvfee0caZlko2RMRERERkWG7beduvr9rV0n2/betrVw4pWXAdXbs2EEikaC5uRmICp0ceeSRdHR08J73vIdHH30UoNff2f++4IIL+OMf/8i73/1urrvuOn71q1/R2toKwNVXX01TUxP/8A//gDGGJ598kt/+9rf89re/5YYbbgCiJPGkk07iJz/5CXPmzOHmm2/m17/+Nb7vM336dL74xS8ydepU9u/fzxVXXMHq1auZOXMmra2ttLW1leSc5VI3ThERERERqVoLFy7k6KOP5qSTTuKjH/0oP/zhD9m9e/eg2+3Zs4f29nbuvPNOzj//fN72trdx3333AVESd99993HWWWf12uad73wnTzzxBLvi5PYPf/gD8+fPZ86cOdx7772sX7+eu+66i7vvvps3v/nNXH311QDccMMNNDY2cv/99/O1r32Nxx9/vMhnoW9q2RMRERERkWG7cErLoK1vpeS6Lt/+9rdZtWoVjz/+OA888AC33HILN95444DbNTQ08K53vavn73POOYerrrqKCy+8kD/84Q+0t7czZ86cXtuMHz++Jym88MILufvuuznnnHMAePDBB1m5ciVnn302AL7v09TUBMCjjz7KlVdeCUBrayvveMc7ivb+B6JkT0REREREqt6CBQtYsGAB559/PkuWLOGFF14gDMOe15PJZK/1x48f32uewOOPP57Ozk6stdx99909SVu+c845hy996UucfvrpPPbYY3z1q18FoknRL7vsMs4999xXbJMbx2hSN04REREREalaW7du5amnnur5e8uWLezatYv58+eTTqdZt24dQE8XzYGceeaZ/OAHP+Dxxx/nne98Z5/rHH/88Rw4cIBvfOMbvP3tb2f8+PEAnHzyydxxxx3s3bsXgFQq1VMo5oQTTuBnP/sZALt37+aBBx4Y/hseArXsiYiIiIhI1cpkMlx//fVs3LiRcePGEQQBH/vYxzj66KO54ooruPjii5k9ezave93rBt3X2Wefzdve9jbOOeecniSuL2eddRbXXnstP/7xj3st27NnDxdccAEQteYtXbqUhQsX8pGPfIRPf/rTLFmyhNmzZ/PGN75x5G+8AE65mhQLtWzZskOBlxYtWkRDQ0O5w6lJy5YtY/HixeUOQ2RYdP1KtdM1LNVM1+/Y9Nxzz3HkkUeWO4wR6+zspLGxsdxh9Cn/HCeTSVauXAlw2OLFi9cWuh+17ImIVLHk448TJpPUHX447vTpvcYeiIiIyNimZE9EpEr5W7fSff/9ACR/9zucpiYShx9Oor2dRHs77gDdT0RERKT2KdkTEalSyT/8AerrGX/66fjbthFs3kz6uedIL18OjoM3ezaJww+n7tWvxosniBUREZGxQ8meiEgV8rdtI/2Xv1D32tfiNjXhNjXB/PnUBwHBrl34mzfjb9xI8ve/J/XEE0z82MdwPK/cYYuIiMgo0tQLIiJVKPnQQ1BXR90RR/Ra7rguXlsb9a9+NeNPPZWGt76V8MABMi++WKZIRUREpFyU7ImIVBl/xw7SK1dSd8wxOImBO2h4s2dDQwOp5ctHKToRERGpFEr2RESqTE+r3oIFg67reB6Jww4js2oVYXf3KEQnIiIilUJj9kREqoi/axfpZ56h7rjjcOrqCtom0d5O5vnnST37LA2aD0tERGrQySefTH19PfX19XR1dXH44YdzySWXcNxxx3HnnXeSTCa56KKLhrTP66+/noMHD/KpT32qNEGPAiV7IiJVJPnQQ+B5JIwpeBt3yhScyZNJL1+uZE9ERGrWddddx4K418tvfvMbPvzhD3PLLbewdOnSMkdWPkr2RESqRLB7N+kVK6g79ljcAlv1ABzHIdHeTvrJJ/F37dI0DCIiUlSpFStIPfVUSfZd/5rXUH/MMUPe7pRTTuHpp5/mlltu4YgjjujVQnfzzTfz61//mnQ6zcyZM/niF7/I1KlT2b9/P1dccQWrV69m5syZtLa20tbWVuy3NKo0Zk9EpEp0P/wwuC6JhQuHvG1i/nwA0itWFDssERGRinTMMcewevXqXsvuvfde1q9fz1133cUdd9zBm9/8Zq6++moAbrjhBhobG7n//vv52te+xuOPP16OsItKLXsiIlUg2LOH9PLl1L361bj19UPe3m1sxJ05k9SKFTScdBKO45QgShERGYvqjzlmWK1vpRaG4SuWPfjgg6xcuZKzzz6bIAgIw5CmpiYAHn30Ua688koAWltbecc73jGq8ZaCkj0RkSqQfOQRABKvetWw95Fobyf18MP4GzaQmDevWKGJiIhUpGeeeYYj8uajDcOQyy67jHPPPZfOzk4aGxt7vVZr1I1TRKTCBfv2kXrqKeoWLRpWq15WYt48SCQ0556IiNS8Bx54gDvvvJOLL7641/KTTz6ZO+64g7179wKQSqV4/vnnATjhhBP42c9+BsDu3bt54IEHRjfoElDLnohIhUs+8giE4Yha9QCcujoS8+aR/stfCJcsGXRCdhERkWry0Y9+tGfqhfb2dm666SaOPfZYHnrooZ51zjrrLPbs2cMFF1xAEAQ4jsPSpUtZuHAhH/nIR/j0pz/NkiVLmD17Nm984xvL+G6KQ/9PLyJSwYL9+0ktW0bdq16F29Aw4v0l2tvJrFlDxlrqjjqqCBGKiIiU34MPPtjva5dffnmvvy+66CIuuuiiV3TjnDhxItdff33JYiwHdeMUEalgyT/+EYKARJESM3fGDJwJE9SVU0REZAxQsiciUqGCgwdJPfEEiVe9Cnf8+KLs03FdEvPnk3nxRYLOzqLsU0RERCqTkj0RkQqVWb0aMhkSRxwBRawQlpg/H8KQ1DPPFG2fIiIytgRBUO4QalYxz62SPRGRCpVZswZn3DjcyZOLul+3pQV3yhTS6sopIiLD0NjYyMaNG0mlUjU5XUG5hGFIKpVi48aNvcYSjoQKtIiIVKAwDMmsWYM3bx6lmP480d5O6rHH8Ldtw5s2rQRHEBGRWjVnzhx27NjBunXryGQy5Q5n2FKpFPUjmNKoFBKJBJMnT6atra04+yvKXkREpKiCHTsI9+/HW7y4JPtPHHooqccfJ7V8OeNPOaUkxxARkdrkui7Tpk1jWpX/WLhs2TKOOeaYcodRUurGKSJSgTIvvgiAW6L/I3XGj8ebM4f0M88QatyFiIhITVKyJyJSgTJr1uC0tOBOmFCyYyTmzyc8cIDMSy+V7BgiIiJSPkr2REQqTOj7ZNauxZs7t6hVOPN5c+dCfb0KtYiIiNQoJXsiIhXG7+iAdBpv5sySHsfxPBKHHkr6+ecJU6mSHktERERGn5I9EZEKk3nxRXAcvCJV4hqIN28eZDL4GzeW/FgiIiIyugqqxmmMWQDcCkwBdgIXWmtfyFvHA64DTgVC4Gpr7ffi1z4HfATYFK/+iLX274vxBkREak1mzRrcGTNw6upK2o0T6EkoMxs2kDjssJIeS0REREZXoS17NwI3WGsXADcA3+1jnfOBw4EjgBOAzxljDs15/TZr7bHxP0r0RET6EHZ14W/aVPLxellOQwPOpElq2RMREalBgyZ7xphpwHHAnfGiO4HjjDFT81Y9D7jZWhtYa7cD9wDvLWawIiK1LrN2LYQh3vTpo3ZMt60Nf9OmwVcUERGRqlJIN865wEZrrQ9grfWNMZvi5dtz1psHrMv5e328TtbfGGNOAbYA/26t/dNQAl25cuVQVpchWrZsWblDEBm2Wrp+pz7zDBMTCdbu3o2/ffvgGxRBo+/TfOAAz/zpT6Tq60flmNJbLV3DMvbo+pVqVuvXb0Fj9orgRuAqa23aGPMO4F5jzJHW2p2F7mDRokU0NDSULsIxbNmyZSxevLjcYYgMS61dv/sfeQRn3jza29tH7Zh+ayvd69axsLmZuiOPHLXjSqTWrmEZW3T9SjWrpus3mUwOq/GrkDF7G4DZcQGWbCGWWfHyXOuBQ3L+npddx1q7xVqbjv/7t/HyRUOOVkSkhgW7dxPs3o03e/aoHtdtbQXXJbMh/7YuIiIi1WzQZM9auw1YDiyNFy0FnorH5eX6KXCJMcaNx/OdBfwPgDGm58nFGHMscChgRxy9iEgNyaxZA4A3bdqoHtfxPNyWFhVpERERqTGFduO8FLjVGPNZYDdwIYAx5n7gs9baJ4DbgdcB2SkZvmCtXRP/95eMMYsBH0gB77fWbinSexARqQnpNWtwJk7EmThxVCpx5nLb2si89BJhEOC4moJVRESkFhSU7FlrnydK5PKXL8n5bx+4rJ/tPzDcAEVExoIwCPDXrMFrb8cZ5UQPwJ06Fawl2LkTb2p+sWURERGpRvr5VkSkAvibNxN2d+PNmFGW4/dMrt7RUZbji4iISPEp2RMRqQDlGq+X5UyaBPX1+CrSIiIiUjOU7ImIVIDMmjW406bhlGmKGcdxcKdM0eTqIiIiNUTJnohImYWpFP769Xhz5456YZZcXlsbwfbthOl02WIQERGR4lGyJyJSZpl16yAIcKdPL2scblsbBAH+FhVLFhERqQVK9kREyiyzZg14Xk+RlHJxVaRFRESkpijZExEps8yaNXizZ+M4TlnjcCdMwGlsVJEWERGRGqFkT0SkjIL9+wm2bcObM6fcoQBR656KtIiIiNQGJXsiImWUnXKh3OP1sty2NsK9ewkOHix3KCIiIjJCSvZERMoos2YNzvjxuJMnlzsU4OXJ1f2NG8sciYiIiIyUkj0RkTIJwzAarzdvHk4Zp1zI5U6ZAo6jcXsiIiI1QMmeiEiZBNu3Ex44gDdrVrlD6eHU1eFMnkxGLXsiIiIUlJQKAAAgAElEQVRVT8meiEiZ9IzXmzatzJH05sVFWsIKaW0UERGR4VGyJyJSJn5HB87Eibjjx5c7lF7ctjbo7ibcs6fcoYiIiMgIKNkTESmTTEcH3syZUGEtaO7UqYAmVxcREal2SvZERMog2L+fcO/eiuvCCeA2N4Pn4SvZExERqWpK9kREyiBb7dKdMqXMkbyS47q4U6aoZU9ERKTKKdkTESmDTEcHeF7FzK+Xz21rI9i6ldD3yx2KiIiIDJOSPRGRMvA7OnCnT8dxK/M27LW1ge8TbNtW7lBERERkmCrzKUNEpIaFvo+/aRPejBkVV5wly21rA1SkRUREpJop2RMRGWX+li3g+xU5Xi/LaWqChoaesYUiIiJSfZTsiYiMsp7iLK2tZY6kf47j4E2dir9pU7lDERERkWFSsiciMsoqdTL1fG5bG8HOnYTJZLlDERERkWFQsiciMsoqdTL1fNlxe2rdExERqU5K9kRERlElT6aez4vHFKpIi4iISHVSsiciMooqeTL1fM64cTgTJ+Ir2RMREalKSvZEREZRpU+mns9ta1M3ThERkSqlZE9EZBRV+mTq+by2NsIDBwj27y93KCIiIjJE1fG0ISJSA3omU58+veKLs2S5U6cCqCuniIhIFVKyJyIySnomU4+rXFYDt6UFAH/z5jJHIiIiIkOlZE9EZJRUw2Tq+ZxEIirSsm1buUMRERGRIVKyJyIySnomU58wodyhDInb3EywfXu5wxAREZEhUrInIjJKeiZTD4JyhzIkbksLwe7dhJlMuUMRERGRIVCyJyIyCqppMvV8bnMzhCHBzp3lDkVERESGQMmeiMgoyFazrKbxelluczMA/tatZY5EREREhkLJnojIKMhs2BBNph4nTtXEmTQJHEfJnoiISJVRsiciMgqqbTL1XI7n4UyerGRPRESkylTfU4eISJWpxsnU87nNzQQ7dpQ7DBERERkCJXsiIiVWjZOp53Obmwn37iVMpcodioiIiBRIyZ6ISIlV42Tq+dyWFgB8zbcnIiJSNZTsiYiUWLVOpp5LFTlFRESqj5I9EZESq9bJ1HM5TU3geQRK9kRERKqGkj0RkRKq5snUczmui9vcjL9tW7lDERERkQIp2RMRKaFqnkw9nypyioiIVBcleyIiJVTNk6nnc5qbCQ8cIOzqKncoIiIiUgAleyIiJeR3dOBOm1aVk6nn6ynSooqcIiIiVaH6nz5ERCpUz2TqM2ZU7WTquVSRU0REpLoo2RMRKZFamEw9l9PYCHV10fsSERGRiqdkT0SkRGqpOAuA4zhRkRZ14xQREakKSvZEREqkFiZTz+c2N+Nv305YA91SRUREap2SPRGREsls2FD1k6nnc5ubobubsLOz3KGIiIjIIJTsiYiUQK1Mpp7PbWkBINDk6iIiIhVPyZ6ISAnU2ni9rGxFzowqcoqIiFQ8JXsiIiVQS5Op9zJuHDQ0ECjZExERqXhK9kRESsDv6MCdPr0mJlPPla3IqYnVRUREKl9tPYWIiFSAnsnUp0+vicnU87nNzQQ7dqgip4iISIVTsiciUmT+5s01NZl6PrelBVIpwn37yh2KiIiIDEDJnohIkdVqcZas7DhEXxU5RUREKpqSPRGRIuuZTH38+HKHUhI9yd6WLWWORERERAaiZE9EpMh6JlOv0TFtTkMDzoQJatkTERGpcEr2RESKKNi3j3DfvpqbTD2f29ysidVFREQqnJI9EZEi6hmvN2VKmSMpLae5mWDnTsIgKHcoIiIi0g8leyIiRZTZsAESCdzJk8sdSkm5zc3g+wS7d5c7FBEREemHkj0RkSLyOzrwanAy9XyqyCkiIlL5avtpRERkFIWZDP7mzbg1Opl6rmyyF6gip4iISMVSsiciUiS1Ppl6LqeuDqepSS17IiIiFUzJnohIkdT6ZOr53OZmgu3byx2GiIiI9EPJnohIkWQ6OnAmTcIdN67coYwKt6WFYPduQt8vdygiIiLSh0QhKxljFgC3AlOAncCF1toX8tbxgOuAU4EQuNpa+728dQzwFPBta+0/jzx8EZHK4W/YgDdrVs2P18tym5shCAh27sSr8XkFRUREqlGhLXs3AjdYaxcANwDf7WOd84HDgSOAE4DPGWMOzb4YJ4PfBe4ZScAiIpUo2LuXcP9+3KlTyx3KqHFUkVNERKSiDZrsGWOmAccBd8aL7gSOM8bkP9GcB9xsrQ2stduJkrr35rz+r8B9wKoRRy0iUmEy2fF6Y6A4S5Y7eTI4Dr4qcoqIiFSkQrpxzgU2Wmt9AGutb4zZFC/PHZk/D1iX8/f6eB2MMUcD7wTeCnxmOIGuXLlyOJtJgZYtW1buEESGrRKu37Znn2Wy67J2x44xlfxMGzeO7jVr+EsFfAbVrBKuYZHh0vUr1azWr9+CxuyNhDGmDrgZuDhOFIe1n0WLFtHQ0FDU2CSybNkyFi9eXO4wRIalUq7fA089BbNm0d7eXu5QRlX35s0Ee/dWxGdQrSrlGhYZDl2/Us2q6fpNJpPDavwqZMzeBmB2POYuO/ZuVrw813rgkJy/58XrzATagfuNMWuBjwGXGGNuGnK0IiIVqNdk6mOM29xMuGcPYTpd7lBEREQkz6Ate9babcaY5cBS4Efxv5+Kx+Xl+ilREvczoqqdZwFvttauB3oGsRhjPgc0qRqniNQKf/NmCIIxNV4vy42LtAQ7duDNnFnmaERERCRXodU4LwUuN8asAi6P/8YYc78x5vh4nduBNcALwJ+BL1hr1xQ5XhGRiuNviDo6eGNkMvVcbksLAJmtW8sciYiIiOQraMyetfZ54HV9LF+S898+cFkB+/rcEOITEal4mY4OnMmTcRoaxswce1nOxIngugRK9kRERCpOoS17IiLShzAMo8nUZ84cc4kegOO6uJMn4yvZExERqThK9kRERiDcu5fwwIExNZl6Pqe5mWDnznKHISIiInlKPvWCiEgt65lMfcqUUTleEIbcmkqzNQgZ58AEx2E8MD7+9wTHYZwD43FY4LlMcJySx+Q2N+O/9BJhKoVTX1/y44mIiEhhlOyJiIyAv2EDJBK4kyeX/Fj7wpALDxzklxm/oPUPdRx+PbGR+V5pO3Fk37u/fTuJ2bNLeiwREREpnJI9EZER8Ds68GbMoNTtZy/6AWcfOMiqIOCTCZczXZckkAS6ge4QkoQ9f+8J4ZqMz1v2d/K/EydwlOeVLLbs9Av+tm1K9kRERCqIkj0RkWEK02n8LVuoe81rSnqcB9MZ/qbzIGEI36rzON6NWurqgYnZlZye/+lhHIfL0xlO3t/JfU2N/FWiNAmfM3EieJ4qcoqIiFQYFWgRERmmUk+mHoYh3+pOseTAQabg8IP6RE+iV4h21+Gm+gQTgFP2d/J/6UxJ4nRcF3fSJPxt20qyfxERERkeJXsiIsNUysnUU2HIpQe7+XhXNye6Dt+r85gzjGIrcxyH79YlmO44nHbgIL9MpYseK6gip4iISCVSsiciMkyZjg6c5uZoMvUi2hYEnLL/IN9Ppfmg5/LVhEfjCKpqTnMcbqzzONxxOLezi5+UIOFzm5sJ9+0jTKWKvm8REREZHiV7IiLDEIZhVJylyJOpL8/4vH5fJ0/4PlclPC5NeLhFmD6h2XH4Vp3HsY7DhZ1d3JQsblKWW5FTREREKoOSPRGRYQh27iQ8cABv2rSi7fPJuHpmKgy5uS7BKUWeMqHJcbimzuNE1+HvD3bzn93Jou07tyKniIiIVAYleyIiw5BZuxYAt0jJXioM+WBnF03AD+sTHOmWZjKHcY7DVxIep7gO/9aV5LNd3UXZrypyioiIVB4leyIiw+CvXYvT1ITT2FiU/X25O8nKIODTCY+2InTbHEid4/CFhMcZrsOXu1M8VIQqnarIKSIiUnmU7ImIDFEYhmTWrsWbMwenCOP1lmd8ru5OcZrr8MYid93sj+c4/EvCYwbwT13d+EV4H05zM8GOHSMPTkRERIpCyZ6IyBAFO3YQdnbizZgx4n2lw5APHeyiBfh4iSY97884x+EfEh7L/YDbilCh021uJty/nzBZvLGAIiIiMnxK9kREhijz0ksAuFOnjnhfX+1OscIP+FTCY3KJu2/25RTX4RjH4cquJPtG2LrXU5FTrXsiIiIVQcmeiMgQZdauxZk0acTj9Z7xfa7qTvJO1+Eto9R9M5/jOHw84bItDLm6a2Qtcj0VOVWkRUREpCIo2RMRGYIwDPGLMF4vE4Z8qLOLicA/j3L3zXxHuS7vdh2uTaZY4wfD3k9PRU4VaREREakISvZERIYg2LaNsKtrxOP1vpFM8aQf8MmER3MZum/m+0jCwwM+NYKpGFSRU0REpLIo2RMRGYKe8XptbcPex198n893JXm76/C2MnXfzDfVcbjIc7knneH/RjAVgypyioiIVI7KeMoQEakSmbVrcSZPxp0wYVjb+2HIJZ1dTAD+pczdN/O9z3OZBfzTweFPxaCKnCIiIpVDyZ6ISIHCMMRft47EnDkwzGTo2mSKx/yAf054tFZA981c4xyHyxMeTwcBPxjmVAw9RVrUuiciIlJ2SvZERAoUbNlC2N2NO8zxeqt8n3/vSnKS63CKW1mJXtbbXIfXOA6f6UqydxgJbc/0C6rIKSIiUnZK9kRECpRZuxYY3ni9qPtmNw3ApxIeToW16mVFUzF47AxDvjyMqRhUkVNERKRyKNkTESlQZu1anJYW3PHjh7ztzak0f/R9Pp7waKvQRC/rSNfhDM/humSK1UOcikEVOUVERCqHkj0RkQKEQUBm3Tq8YYzX6wpD/qMryWLHYUmFdt/Md6nnUQ988uDQp2JQRU4REZHKoGRPRKQA/ubNkEziTZ8+5G1vSqbYGoZcknArtvtmvrZ4KoZfZDI8OMSpGFSRU0REpDIo2RMRKYA/zPF6B8OQr3aneK3rsNitrlvuUs9ltgOfONhNZgitmarIKSIiUhmq68lDRKRMMmvX4ra24o4bN6TtvptMsS0MuaRCJk8figbH4XLPY2UQ8D9DaN1TRU4REZHKUH1PHyIioyz0fTLr1+MOcbxeZ9yq93rX4dgqa9XLeqvrMNeBG7oL75LZU5FTyZ6IiEhZVefTh4jIKPI3b4ZUCm+I8+t9J5liRxjyoSps1ctyHYf3ui5/8gOWZfyCtumpyLl9e4mjExERkYFU7xOIiMgoybz0EgDelCkFb3MgDPl6d5I3uA7HVGmrXtbpnssE4IZkquBtVJFTRESk/Kr7CUREZBT4a9fitrXhNDQUvM23kyl2hFTlWL18TY7Duz2X/0ql2RYUNu+eKnKKiIiUX/U/hYiIlFDo+2Q2bBjS/Hr74la9E12HRVXeqpd1ruuSAm5JpgtaXxU5RUREyq82nkJERErE37gR0mncadMK3uaG7hS7Qqp6rF6+w1yH17sONyZTpAtIelWRU0REpPxq50lERKQEesbrFTi/3t4w5Jpkkje7DkfVSKte1nmuy6Yw5J4CpmFQRU4REZHyq60nERGRIsusW4c7bRpOfX1B61/fnWJ3CJd4XokjG30n9EzDMHihFlXkFBERKT8leyIi/QgzGfwNG/Bmzy5ovN6eIOSb3UlOch0Wus4oRDi6PMfhXNflEd/nqQKmYVBFThERkfJSsici0g+/owMyGdzp0wta/7pkkr3Ah2qwVS/rdM9lPIVNw6CKnCIiIuWlZE9EpB+ZtWvBcQqaX293EHJtd4qTXQdTg616WRPjaRh+kkqzfZBpGHoqcqorp4iISFko2RMR6Udm7dpovF5d3aDrfjOZZB+13aqXda7rkgS+P8g0DD0VObdtG4WoREREJJ+SPRGRPoTpNH5HR0Hj9XYFIdd3p3i763BEDbfqZc2Pp2H4TjJFZoBzo4qcIiIi5aVkT0SkD5n168H3Cxqvd00yyQHgg2OgVS/rva7LxjDk3gGmYVBFThERkfJSsici0oeMtZBI4E2dOuB6+8OQ73SneJvrcPgYaNXLeqPrMMeBbw0yDYMqcoqIiJSPkj0RkTxhGJK2lsQhh+A4Aydw30+m2Atc4I2t22l2GoaHfZ/lA0zDoIqcIiIi5TO2nk5ERAoQbNlCuG8f3iGHDLheJgy5rjvFcY7DUe7Yu50WMg2DKnKKiIiUz9h7OhERGUTaWgC8Qcbr/SydYX0Y8r4x1qqXNclxWBJPw7Czn2kYepI9VeQUEREZdWPzCUVEZABpa/Fmz8ZpaOh3nTAMuaY7ySEOvGkMjdXL917XpRv4fqrvaRicpiZV5BQRESkTJXsiIjmCPXsItmzBO/TQAadceCTj84QfsNRzcQcZ11fL2l2H17oO3+7uexoGVeQUEREpHyV7IiI50qtWAeDNmjXgel/vTtEMnDYGx+rl+2vXpSMM+UU/0zCoIqeIiEh56ClFRCRHxlrc1lbcxsZ+11nl+/wyk+Fcz2XcGG7VyzrRdZjhRJVJ++K2tEQVObu6RjkyERGRsU3JnohILOzuJrN2Ld5hhw3YhfPa7hR1wLljtDBLPs9xOM11+U3GZ1MfhVrc1lYAfI3bExERGVV6UhERiaVXr4YgwJs9u991tgcBt6XSnOY5TFGrXo8lrksA/LiPQi3ZZC+zefMoRyUiIjK2KdkTEYllrMWZMAG3paXfdb6bTNMNLHW90QusCsxzHV7jONyaTBPmtYq648fjjB9PsGlTmaITEREZm5TsiYgAoe+TfuEFEocdRn/tdd1hyLeTKU50HQ4bw9Mt9Oc0z8UGAY/6/itec1ta1I1TRERklCnZExEB/HXrIJnEnTu333V+nEqzfQxPoj6Yt7sO44Hbkn135Qx27iTsIxEUERGR0tATi4gIkH7+eUgk8KZN6/P1IAy5pjvFkY7D8Rqr16dGx+Fk1+GuVJqu/K6cLS0QBASab09ERGTUKNkTkTEvDEPSq1aROOQQnH4Suf/NZLBBwPs8t991JOrKuRe4N2/OvZ4iLVu2lCEqERGRsUnJnoiMecHWrYR79+LNm9fvOtd0p5jhRF0VpX+LHYdZwG15c+45kyaB56lIi4iIyChSsiciY176+ecB8GbM6PP1JzM+v8/4nOe6JNSqNyDXcTjNc3kg49ORM+ee47pRkRa17ImIiIwaJXsiMualV63CnTULp6Ghz9evSSZpBM5SYZaCLPFcQuBHeYVa3JYW/G3bXjE1g4iIiJSGnlxEZEwL9u4l2LyZxKGHQh9JyIYg4KepDGd5Lk1q1SvInLiIza2pVK/Ezm1thWSScN++MkYnIiIydijZE5ExLW0tAN6sWX2+/q3uaOzZeWrVG5LTPJfVQcgfc6ZayBZpUVdOERGR0aGnFxEZ0zKrVuG0tuI2Nb3itf1hyPeSKd7mOsxUq96QnOw6TKD3nHtuSwsAvoq0iIiIjAoleyIyZoXd3WReeonEYYf12YXzh8kU+0CTqA/DBMfh7Z7DT1NpDsbn1qmrw5k0SS17IiIio0RPMCIyZqVXr4YgwJs9+xWv+WHIt5IpjnUcjnJ1qxyOJa7LfuDuVO/WPX/r1vIFJSIiMoboCUZExqzMqlU448f3dC/MdX86w5og5G/Uqjdsr3Ec5jpwW26y19pKuHcvYTJZxshERETGBj3FiMiYFPo+6RdewJs/n75G412XTDEDeIsmUR8213FY4rr8v4zPOj+ac6+nSIta90REREouUchKxpgFwK3AFGAncKG19oW8dTzgOuBUIASuttZ+L37tYuDjQAB4wM3W2uuK9SZERIbKX78eurvx5sx5xWsr4knU/9HTJOojtcRz+a4f8KNUmivGN7xcpGXzZhLz5pU5OhERkdpWaMvejcAN1toFwA3Ad/tY53zgcOAI4ATgc8aYQ+PX/gc4xlp7LPAG4BPGmKNHEriIyEikn30WEgm8adNe8dr1yRTjgTPUhXPEZjkOr3VfnnPPmTABGhpUkVNERGQUDPokY4yZBhwH3BkvuhM4zhgzNW/V84ha7AJr7XbgHuC9ANbafdbabKm7CUAdUeufiMioCzMZUs8+S+KII3Dyiq9sCwLuTKU53XOZpFa9ojjNdXkpCHk44+M4Dm5rq7pxioiIjIJCunHOBTZaa30Aa61vjNkUL9+es948YF3O3+vjdQAwxpwBfBloB/7NWvvMUAJduXLlUFaXIVq2bFm5QxAZtqFev42bNjGzu5vtEyZwMJ5UPes7jRNJNTbxpm1b2ZAzIbgM3xE4TJg6jW/t2Mn0fXuYBDRt386TTzxBqIQa0D1YqpuuX6lmtX79FjRmrxistT8Hfm6MmQfcY4y539q8p6wBLFq0iIaGhtIFOIYtW7aMxYsXlzsMkWEZzvXbaS3+xInMXrSoV3GWZBhy194DnOjA62fNKm6gY9wpmQy/dsZzy4xpjEskSG7ezDHz5vXZjXas0T1YqpmuX6lm1XT9JpPJYTV+FTIgZQMwOy7Aki3EMitenms9cEjO3/P6WAdr7XrgMeDdQ45WRGSEggMHyKxeTcKYV1ThvCuVZluo6RZK4TTXpRP4WSr9ckVOTa4uIiJSUoM+0VhrtwHLgaXxoqXAU/G4vFw/BS4xxrjxeL6ziAqzYIxZmF3JGNMGvBUYUjdOEZFiSD/9NIQhicMO67U8DEOuS6Zodxxeq66FRXeM4zAvnnPPmTwZPE9FWkREREqs0G6clwK3GmM+C+wGLgQwxtwPfNZa+wRwO/A6IDslwxestWvi//47Y8wpQBpwgG9Za39TpPcgIlKQMAxJrViBO3MmbmMjhC/XiXo447PcD7gi4eEo2Ss6x3E4zXX5TsZnXQjTm5vVsiciIlJiBSV71trniRK5/OVLcv7bBy7rZ/uPDzdAEZFiCbZsIdi2jYa3vKVXogfRJOrNwKmaRL1kTvVcvuMH3JFK84nWVvyOjmg6BiXXIiIiJaGBKSIyZqRWrADPe8VE6mv8gHvTGc7xXMYp8SiZWY7DX7kOt6dSOM3NhF1dhAcOlDssERGRmqVkT0TGhND3ST/zDIn2dpxE704N306m8ID3qDBLyb3LdVkdhDzX3AyoSIuIiEgp6clGRMaEzAsvEB48iNfe3mv5vjDk+8kU73AdpqlVr+ROdh3GA7dOaARQkRYREZESUrInImNCasUKnAkTXjGv263JFPuB89SqNyoaHYeTXYfbQweamtSyJyIiUkJ6uhGRmhccPEhm1SoSCxf2mlvPD0O+lUxxrONwlKvb4Wh5l+eyB9jS3IK/dWu5wxEREalZeroRkZqXXrkSggDv0EN7Lb8/nWFNoEnUR9vxjsN04KGJEwl37yZMpcodkoiISE3SE46I1Lz0ihW406bhTZrUa/l1yRQzgLdouoVR5TkO7/Jc7pkYfR5q3RMRESkNJXsiUtP8bdvwN20iYUyvufWWZ3x+n/E5z3NJqDDLqFviuqyYNBlQRU4REZFSUbInIjUttWIFuC6JuXN7Lb82mWICcIa6cJbFYa5D84QJ7KurU0VOERGREtFTjojUrDAISD/9NN5hh+HU1/cs3xQE/FcqzRmeyyS16pXNuxIuyydPZv/mzeUORUREpCYp2RORmpVZs4bwwAHq2tt7deH8TjJFBk23UG6nuC7PTpqMt2MHYRCUOxwREZGaoycdEalZ6RUrYNw43Jkze5Z1hiE3JVO81XWYo1a9smp2HNLNzdT5PumdO8sdjoiISM1RsiciNSns7ib9/PPUGdNrbr3bU2l2hbBUrXoVYU5zCwBPvfRSmSMRERGpPXraEZGalH72Wchk8A47rGdZEIZc251kkeNwjFr1KsLC5smkHIeX1m8odygiIiI1R8meiNSk1PLluK2tuM3NPcvuT2dYHYQs9VwcJXsVoc7z2DJpEvXbt7Mn45c7HBERkZqiZE9Eao6/eTN+RweJo47CySnM8s14EvWTNYl6ZWlu5qi9e7hrz55yRyIiIlJTlOyJSM1JPvYY1NWRmDevZ9mTGZ//0yTqFWlcczMzkknu3aj59kRERIpJyZ6I1JSgq4v0ypXULVyIk0j0LL82maIROFOFWSpOsqUVgGDTJlYnk2WORkREpHboqUdEakr6qacgkyGxYEHPso1BwF3xJOoT1apXcZKtrQSuywm7dnLbrt3lDkdERKRmKNkTkZoRhiGpJ57AmzMHd+LEnuXfTqYIgL9Wq15FCj2PZEsLb9+zm9t27SbIGWcpIiIiw6cnHxGpGZnVqwl27yZx5JEQJwwHwpCbujWJeqXramtj4a5dbOnq5qEDneUOR0REpCYo2RORmpF67DGcxka8mTN7lt2WTLMHeJ9a9Spad9tUvCDghH17uFVdOUVERIpCTz8iUhP8XbvIrF4dTbcQt+D5Ych1ySRHOw5Hu7rdVbKuKVMAeN/+/fx0z14O+JpzT0REZKT09CMiNSH1xBPguiTa23uW/TKd4cUgVKteFQgaGkhNmsSbd+7kQBDw0z17yx2SiIhI1dMTkIhUvTCdJvXUUyQOPxy3oaFn+Te7U8wC3qJJ1KtC15Q2pm7dwmF1CW7esavc4YiIiFQ9JXsiUvXSzzwD3d0kjOkpzPJExuchX5OoV5Outja8VIqL/Qx/OniQZ7u6yx2SiIhIVVOyJyLVLQxJPv44blsbbjzuC+DaZJJG4Ax14awaXW1tAJy2dw91wC071bonIiIyEnoKEpGqNm7PHoItW6hbtAgnbtXbEAT8dyrD2Z5Lk1r1qkamsZHMuHFM27SJNzU1ctuu3SSDoNxhiYiIVC0leyJS1Sa/9BI0NODNnduz7IbuaBL196pVr7o4Dl1tbYzftIlTJ01kp+9zz9595Y5KRESkaulJSESqVnDgAE2bN1N35JE48dQKe4KQm5IpTnEdZqlVr+p0tbWR2L+fN2YyzEwkuHnHznKHJCIiUrWU7IlI1Uo9+SROGJI44oieZTenUuwHLvC88gUmw9bdNhWAyZs2sWTSRH53oJM1yWSZoxIREalOSvZEpCqFQfD/2bvv8Ejv8tzj33d6L5pRl1bbpOFKFcUAACAASURBVNnusm7rvjY2toHYgAnNmJgcQglwknAwhBOaEwjEhGZMIECwwY4bNhCbYkIxxIC9fdfbRtulVa8jaTR93vPHynvW9vbVajTS/bkuXSvN/DT7yJ4dvfc8v0J2/XoylZVYvF4A0qbJV9NZVlkMYjpuoSxlgkGKNhu+gwd5dcCPBfiPgaFSlyUiIlKWFPZEpCzl43HMkRHSDQ2Hj1t4MJujxzR5h9bqlS+LhXQkgqfjIJU2G5d4PHxvYJD8xP9jEREROXm6IhKRspRZuxYjECA10dUrmCb/ms6wxDC4QGv1yloqEsU+MIAtk+HGgJ/OfJ6fj4yWuiwREZGyo7AnImWn0NdHYd8+7MuWUcjnAXgyl2dX8VBXz1DYK2upaBQDCHR1cYnXQ9Rq5TvaqEVEROSUKeyJSNnJPv88WK3Y5s0DwDRN7k5naDRgtdbqlb10pALTMPAdbMdmGLza7+enI6N05nKlLk1ERKSsKOyJSFkxUymyW7ZgX7QIw2YD4Nl8gTWFIm+3WrCqq1f2TJudTCiE92AHADcG/BSA+7RRi4iIyClR2BORspLduBFyOayx2OHb7k5nqABeY9FL2kyRikZx9HRjKRSod9hZ6Xbx7YEBitqoRURE5KTpykhEyoZZLJJZuxZrQwPWQACAVquNn+cLvNlqwaWu3oyRjlZiKRTw9fUBcGMgwP5sjt+OjZW4MhERkfKhsCciZSPf2oo5PIx96dLDxy3c5/PhAd6o4xZmlFQ0AoD/4EEArvB6CFgsfKd/sJRliYiIlBVdHYlI2cisWYPh92OpqQGgrVjkpw4Xt1gtBNXVm1EKLjdZnw/vwXYAHBYL1/t9PJEYoX9iB1YRERE5PoU9ESkLhd7eQ8ctLF/Oi7Huq+ksJvBWdfVmpHQ0iquzE2Oii3tDwE/WNHlgUBu1iIiInAxdIYlIWcg+/zzYbIePWxgsmnw3k+XKbIYadfVmpFQ0ijWdxj10KNwtcDpZ6nLynYFBTG3UIiIickIKeyIy7R3tuIVvZrIkgT/Thh0zVioSBcDf0XH4tpv8fralMzw3Pl6qskRERMqGwp6ITHvZjRshnz983ELKNLknk+Vyi0FTQeu3Zqqc30/B4cA3sW4P4Gq/D7dhaKMWERGRk6CwJyLTmlksklmzBmtjI1a/H4D7szn6TZPbtFZvZjMMUtEo7s7Owzd5LBZe5ffx8NAwI4VCCYsTERGZ/nSlJCLTWr61FTORwL5kCZgmBdPkS+kMyw2D87VWb8ZLRaPYEwmcyeTh224K+Bk3Te7XRi0iIiLHpbAnItNa5vnnMQKBw8ctPJHLs69o8g6rBUNhb8ZLRysB8B/R3VvkcrHc5eSrvf0UtFGLiIjIMSnsici0VejpobB//+HjFkzT5O50hrkGXGlR0JsN0uEQRav1Jev2AN4QDLInm+WnIyMlqkxERGT6U9gTkWkrs2bNS45beDqfZ2OhyO1WK1Z19WYHi5VMRQWeI3bkBLjC56XGZuPLvf0lKkxERGT6U9gTkWmpOD5ObssW7IsXY1itmKbJZ1MZaoEb1dWbVVLRKI6+PmzZ7OHbrIbB64MBnhlLsmk8VcLqREREpi+FPRGZlnITxy3YJo5b+F2+wHOFIrfbLNjU1ZtVUtEohmni7+5+ye03Bfy4DYOv9Km7JyIicjQKeyIy7ZjFIpm1a7E2NmLx+QD453SGKPA6i162Zpt0JIJpGPgP7H/J7T6rlZsCfh4aGqY7lytNcSIiItOYrppEZNp5+XELf8rn+U2+wG1WC0519Wadot1BqrKSwK5d8LLdN28JBsmZJv/WP1Ci6kRERKYvhT0RmXaya9e+5LiFz6cyhIDX6xD1WWusvh778DCewcGX3N7gsHOZ18O/9Q+QLhZLVJ2IiMj0pCsnEZlWCgMD5Pfuxb50KQawMV/gZ/kCb7Na8KirN2uN1TcAULGr9RX3vSEYpC9f4MGh4akuS0REZFpT2BORaSW7bh1YLFjnzgXgC+kMfuBN6urNagW3m1QkQqD1lWHvXLeLZqeDL/f2YeqQdRERkcN09SQi04aZy5HdtAlbczMWh4PthQJP5PL8udWCT129WW+svgFnfz/u4Zd28AzD4I3BINvSGX49Olai6kRERKYfhT0RmTZyW7dCOo2tpQWAL6SzuIC3qKsnwFhDPQDhXbtecd9qv48Kq5Uv6xgGERGRw3QFJSLTRnbdOizRKJZIhD2FIo9kc9xqtRBSV0+AvNdHJhQmeJSpnA7D4OZggJ+NjBJPp0tQnYiIyPSjsCci00K+o4NCZye2pUsxTJO70xmswNvU1ZMjjDbU4+rpxjE6+or7XhcI4DAMvtqnYxhERERAYU9EponsunVgt2NrbKS9WOT72Ry3WC1E1dWTI7y4K2dk9+5X3Be2WbnO7+P+gUEG8/mpLk1ERGTaUdgTkZIrplLktm7FvngxhtXKl9JZTOA2dfXkZXKBANlAgGBr/Kj3vyEYZNw0+fbA4FHvFxERmU10JSUiJZfbtAnyeWzNzfQUi3wnk+U1VoNadfXkKEbr63F1dmIfH3/FffOdDi5wu7mnr5+cjmEQEZFZTmFPRErKNE2y69Zhra/H4vfzlXSWLHC7xVrq0mSaStY3YJgmFXteOZUT4I2hIB25PI8PJ6a4MhERkelFYU9ESiq/dy/FwUFsS5YwWCjyzUyW6y0Gcyzq6snRZUIhcl4voaPsyglwkcfNHLudL+mQdRERmeUU9kSkpLLr1mF4PFjr6vh6JsMY8E6runpyHIbBWH0D7vZ2bEc5ZsFiGLwhFGDteIo/JV851VNERGS2UNgTkZIpJhLk43FsS5YwYprck85yjcVgobp6cgJjDfUYxSLh/fuOev/1fj8hq4XP9vROcWUiIiLTh+1kBsVisRbgfiACDAC3x+PxXS8bYwW+BtwAmMDn4/H4dybu+wTwFiA/8fHxeDz+9GT9ECJSnrLr14NpYlu4kHvSWYaBd6mrJychXREh73YTisfpW7T4Ffe7LRbeHArxrYFB/pRMssrrLUGVIiIipXWynb1vAvfG4/EW4F7gW0cZ83ZgIdAMrAI+HYvF5k7ctwa4MB6PnwO8C3gkFou5z6RwESlvZqFAdsMGrAsWMOJw8pV0htUWg5i6enIyDIOx+no8Bw5gzeWOOuTmYIAKq5VPdHZPcXEiIiLTwwnDXiwWqwLOBx6auOkh4PxYLFb5sqFvBr4dj8eL8Xi8D/gx8CaAeDz+dDwef3HhxBbA4FCXUERmqdzOnZjJJLZYjK+m0ySA/6WunpyCsfoGLPk8of37j3q/22LhbeEQvx5L8szo2NQWJyIiMg2czDTORqAjHo8XAOLxeCEWi3VO3N53xLg5wIEjvm6bGPNytwN74vH4wVMpdOvWracyXE7R+vXrS12CzDL1f/wjdo+H+OgoX7HauTSXw93bTftpPFZ7++l8l5Q7wzSpttlxbNxA/Bi7bsaACouNv9u9l38v5piufWO9Bks50/NXytlMf/6e1Jq9yRKLxa4C/hG47lS/d9myZTidzskvSli/fj0rV64sdRkyixR6exkbHMRx2WX8pLaOsXSWD3i9NPp9p/xY7e3tNDYe7X0lmQ3Ge3uoOniQxfPnU7Qd/VfaOxMjfLmvn8HmFq4P+Ke4whPTa7CUMz1/pZyV0/M3k8mcVvPrZNbstQP1ExuwvLgRS93E7UdqA5qO+HrOkWNisdgq4AHglng8Hj/lSkVkxsiuWwdWK6Nz5nBPOsurLAbNWqsnp2GsvgFLLkeove2YY24M+Km12fiHrm6duyciIrPKCcNePB7vBTYBb5246a3Axol1eUd6DHh3LBazTKznuwV4HCAWi10IPALcGo/HN0xW8SJSfsxcjuwLL2BrbubLhSJjwF9qrZ6cpvHqKop2+zEPWAewGwbvqAixdjzFUyOjU1idiIhIaZ3sbpzvBT4Yi8VagQ9OfE0sFvtZLBa7YGLMD4C9wC7gOeCueDy+d+K+bwBu4FuxWGzTxMfyyfohRKR85LZvh3Sa5IIFfD2d5TqdqydnwmJlrLYW3549UCwec9h1fj+Ndjuf7OqmqO6eiIjMEie1Zi8ej+8ELj7K7Tcd8XkBeN8xvv/C0y1QRGaW7IYNWMJhvujxkcxk1dWTMzbW0ECgrY1QRwfDx1i/aZvo7n2up48nhhPcGg5NcZUiIiJT72Q7eyIiZ6zQ10ehrY3MokV8I5Pl1RaD+erqyRkar66haLUSju887rhrfD7mOex8qruHgrp7IiIyCyjsiciUyW7YABYL91RVk0Jr9WRymDYbY/UNBHbswJZKHXOc1TB4Z0WY7ekMjwwNT2GFIiIipaGwJyJTwsznyW3eTGH+fL5oGtxgMZirrp5MkqFFMSy5HNWbNh533BVeLwsdDj7V3UNe3T0REZnhFPZEZErkdu7ETKV4qKGRNPAudfVkEmWDIZJ1dUQ2bMCSzR5znMUw+IuKMLszWX4wODSFFYqIiEw9hT0RmRLZDRsoBgLc6Q9yk8WgSV09mWQDixdjzWSo2bz5uOMu9XpY7HTyme4essfZwVNERKTcKeyJyFlXGByksG8fv503n4xh8C6bunoy+TIVEcarq4muX4eRyx1znDHR3TuQzfEf6u6JiMgMprAnImddbsMGTMPgw1U1vMZq0Gioqydnx+CixVjHx6navu244y70uFnucvFP3T2k1d0TEZEZSmFPRM4qs1Agu2kT8foG2t1u7tBaPTmLUpWVpCMRqtasgULhmOMMw+COijAduTzf6h+cwgpFRESmjsKeiJxV+dZWzGSSz9bV81qrQYO6enI2GQYDixdjGx2lcufxz907z+PmAo+bz3T30JfLT1GBIiIiU0dhT0TOquyGDSTcHv67qlpdPZkS4zW1ZEIhqp5/Dk4wRfOvoxFGCwXu7OyaoupERESmjsKeiJw1xeFhcrt38+3GRm622ahXV0+mgmEwuGgxjuFhIrt3HXfoXIeDN4dC3Dc4xO/HxqaoQBERkamhsCciZ01240ZM4NE5Tdxh08uNTJ2xhnqyfj/Vzz0HJzg8/baKEHU2G+9r79BRDCIiMqPo6ktEzgqzWCS5cSO/qaziCr+fiLp6MpUMC4OLFuHs7ye8b99xh7osFj5QGWF7OsOX+/qnqEAREZGzT2FPRM6K/O7d2EZH+eHcedxm1UuNTL3ROU3kPF6qnz9xd2+V18uVXi+f6ephfyY7RRWKiIicXboCE5GzomvNGnodThoaGvCpqyelYLEwFIvh7uoicPDgCYe/PxoB4EMHO852ZSIiIlNCYU9EJl1hZATf3r38pKmJm9XVkxIamTeXgstF7fPPnXBstd3GX1SEeXJklJ8MJ85+cSIiImeZrsJEZNK98PzzWE0T28KFuNTVkxIyrTYGW1rwtLXh6zrx8QpvCAVZ4HDwoYOdJAvarEVERMqbwp6ITKp8sYhj82bWRiu51OcrdTkiJOYvoOBwnFR3z2YY/O/KKG25HHd190xBdSIiImePwp6ITKr/3vIC9ckkoy0teoGRacG02xlqacG3dy8VrfETjl/udvGagJ8v9faxNZWeggpFRETODl2LicikyRaLjKxfz4jdwbz6+lKXI3LYUGwR6UiExqd/iWN4+ITj3x2pwGux8L72gxRPsJOniIjIdKWwJyKT5v6DHazu7KBr4ULypS5G5EgWC10XX4JpwIKnnsTIH/8ZGrRaeU+0gmeT49w/ODRFRYqIiEwuhT0RmRRjhQK7163DWSzimzev1OWIvELe66Xnwgtx9fbS+Pvfn3D8q/1+VrhcfKSji4EThEMREZHpSGFPRCbFV3v7eMO+fQxWVZH2+0tdjshRJevqGW5pIbJpI+HW1uOOtUxs1jJcKPDRzhPv5CkiIjLdKOyJyBkbyOf59Y44i8ZGMVtiFIvasl6mr77ly0lXRGj85dMnXL833+ngzaEg3x0Y0tl7IiJSdhT2ROSMfb6nlzfu30vebmespqbU5Ygcn8VK1yUXY5gm83/6FEahcNzh74xUsMjp5C/a2tmfyU5RkSIiImdOYU9EzsjBbJb7Oru5tbOD9MKFZHWIupSBvNdH94UX4u7pOeH6PYdh8ImaKvIm/Pn+A2TVuRYRkTKhsCciZ+STXT3cfLAdV6HAqDZmkTKSrG9guLmZyMYNhHbvOu7YOrudj1RFWTue4qOd3VNUoYiIyJlR2BOR07ZhfJz7Bof4UEc7mUiElE8bs0h56V++gky4gjlPP40jcfw1eVf6fLwxGOArff38WOv3RESkDCjsichpMU2TvzvYxaWjIzQNDDDe0kJBh09LmTGtVjovuQSjWDy0fu8ERyz8VTTCYqeTO9ra2af1eyIiMs0p7InIaflxYoTfJZN8qruLotXKWG1dqUsSOS15n4+eCy7E3d1N8+OPY02ljjnWYRj8Q00VBa3fExGRMqCwJyKnLFMs8pGOLpZYLJy/dw/j8xeQsVpLXZbIaRtraKD7kkvwdHUSe/ghHENDxxx7aP1eJevGU9yp9XsiIjKNKeyJyCn7et8Ae7JZPpsYwpbJkJw/D03glHI32jiHjquuwjY+Tuzhh/B2dhxz7BU+L7cGA3y1r58faf2eiIhMUwp7InJK+nJ5/rG7h1UeDxe2xskFgySDoVKXJTIpUtFK2ldfg2mzs/CxxwjHdx5z7F9FIyxxOrnjgNbviYjI9KSwJyKn5NPdPYwVi3zYauDr6GAsFtPGLDKj5Px+2lavJltRwdyf/pSaNc/DUZ7j9on1e0W0fk9ERKYnhT0ROWnbU2m+1T/AzcEAy+M7MS0WxhoaS12WyKQrOp0cvOJKxpqaqH32WZr++5dQKLxiXK3dzp0T6/c+0tlVgkpFRESOTWFPRE7ahzs68VgsvDMYoGLbNlJz55Kx2UpdlshZYVqtdF14EUNLllCxdSvNP/oR1nT6FeMu93l5UzDA1/oG+Hb/QAkqFREROTqFPRE5Kb8YGeUXo2O8Ixyicf9+7KkUY/PnY2oKp8xkhkH/0mX0Xngh3vY2ltx/H+EdO14xrfPd0QirPB7e097Bw0PDJSpWRETkpRT2ROSE8qbJhzs6abTbuSUUJPrCC+R8PpIVkVKXJjIlEnPncfDaaym43cz9+c9ofuwxXAP/v4tnNww+WVPFuW4X79jfxk8TIyWsVkRE5BCFPRE5oX/vH2B7OsNfRSrwjozgP7Cf8ZYW8urqySySDlfQdvVq+i+4EHdfL4t+8H3qfv87LNlDO3G6LBb+sbaGZqeTW/cd4JnRsRJXLCIis53Cnogc13C+wCe7ejjf7eIyr4fIli1gGIw2NZW6NJGpZxgMzZvH/lffwNi8eVSvW8eS+75HqLUVTBOvxcI/19VQZ7fzur37WZMcL3XFIiIyiynsichx/VNPD4OFAu+LRrAUCkS2vkBq7lzSdkepSxMpmaLTSff5Kzl4zbWYDgfznnqShU88jnNwkKDVyr/U1RC0Wrhhzz5eSKVKXa6IiMxSCnsicky7Mxm+1jfATQE/C51OQrt2HdqYpblZG7OIAKlIhAOrr2Hg/JV4OztZfP99NP3i59SOjnJ3XS12A67bvY/dmUypSxURkVlIYU9EjunDBzuxAXdUhAGIbt5ENhRiLFxR2sJEphOLhcEFC9h/w42MxBYRam1lyX3f45Lf/Jqvez1kTZNX7dpL+8TaPhERkamisCciR/Xj4QT/NTLKOyvCRGw23H29+Do7SS5aRKFYLHV5ItNOweWid/lyDtx4I6OxGKGdO7nugR/wmx3b8I4keNXuffTm8qUuU0REZhGdhiwirzBaKPDBgx0sdDh4YygIQHTzZoo2G2P1DSWuTmR6y7vc9CxfwcDCZiK7djF3VyvPtsZ5qKGRd44n+c9zVxC26deviIicffptIyKv8ImuHjpyeb7eUIfNMLBkMoR37GB84UIyFk0IEDkZebebnhUrGGhuJrKrlbfs3s2ft7fx5PZtXHb99SyoqS51iSIiMsMp7InIS6wbH+eevn5uCQZY4nIBULF9O9ZcjtEFC9G2LCKn5lDoOwdbczOFeCs37NmN9d+/xa7FS5h/7TWlLk9ERGYwhT0ROSxvmryn7SAVVivvqpjYhMU0iW7eRLq6mnGfD7ReT+S05N0eOPdcNrW0sG/HTm7euYORHdupqq+nMHcu1kik1CWKiMgMo/lYInLY1/v62ZBK84HKCD7roZcH38GDuAcHScZiFBX0RM5YhcfDkvPP40PX38A35i3A2dXF2L33Mv7EExT6+0tdnoiIzCAKeyICQFs2yz909bDK4+FKr/fw7dHNmyi4XIxV15SwOpGZxW0YfMTnpfOcc1h27fU8urCZ7I4dh0Lfj35EcWio1CWKiMgMoGmcIoJpmnygvYOiafKhygiGYQBgGxsjtHs3o8uWoxPCRCaXYRjcYbMSyKT528VL+dKCZp5s20/ltm3ktm7FsXIlziuvxOLzlbpUEREpU+rsiQg/Tozw5Mgof1ERpsZuP3x79IUXMIpFRufNLVltIjPdRZk037XbSDodLF3YwlOveS22lhay69Yx+rWvkf7NbzDT6VKXKSIiZUhhT2SWG5k4U6/Z6eANE2fqAVAoENmyhfE5c0g7XaUrUGQWWGAx+J7dxnkWg9uw8L+XrSD7ZzdjbWwk8z//w+jXvkbmD3/AzOVKXaqIiJQRhT2RWe4TXd105vL8bWUU28T0TYDg3r04kmMkm5spmjpwQeRsCxoGX7FZeafVwveyORZj8MBFF+N87WuxRKOkf/UrRu+5h+yGDZjaLElERE6Cwp7ILLY2Oc49fQO8Phhgseul3bvKzZvI+f2MRaIlqk5k9rEZBh+wWXnAbqMJg/eNp7nC7uSFK6/CdcMNGB4PqSefZOzb3ya/b1+pyxURkWlOYU9klsqbJn/VfpCozcodL56pN8E5OIC/rY3kokXk1dUTmXLNFoNv2q38o83KgUKRy0aTvN8XIHnddTivugpzbIzk979P8pFHKAwOlrpcERGZphT2RGapr/X1symV5gPR/3+m3ouim7dgWiyMzGkqUXUiYhgGN1gtPOqwcZvVwvezOZaMJvlObR2OP/sz7CtXkt+zh7F77yX1y19qExcREXkFhT2RWWhnOs0nOru5zOvhiiPO1AOw5HJEtm9jfMECslZriSoUkRf5DIMP2aw8aLfRbBh8cDzNqlSGNbFFuG++GdvChWT/9CdG77mHzNq1Ws8nIiKHKeyJzDLpYpG37GvDYTH4m8ro4TP1XhTevh1rJsPYgoWYmsIpMm3Mtxh83Wbln21WugpFVo+Nc0nB5IHzV8JrX4sRDJL+2c8Y++Y3tZ5PREQAhT2RWedjnd1sTqe5s6qSqM320juLRarXrSVdXU0yEChNgSJyTIZh8CqrhcccNj5qszBWNHnveJomi52PX3YFfVddjZnJHFrP99hjFIeHS12yiIiUkMKeyCzyVGKEr/b1c2swwKqXTd8ECLW24kwkGF26jIK6eiLTltswuNVq5UG7lW/brVxqMfj3bI7mYJhbV19L64oV5FtbGb33XtK/+53O5xMRmaUU9kRmic5cjjsOtNPidPDuaOSVA0yT6rVryIbDjFZWTn2BInLKDMPgXIuFu+w2nnTY+KDVwnbgorkLuHr1q9hRV0fmmWcY/cY3yO3YoanZIiKzjMKeyCxQME3esb+NZLHI/62uwvGydXoAgf378fT1Mbp8OQVt8CBSdioMg9ttVh632/iKzUrA6+HSc1fyuksvZ0ehyPijjxL/j++xr6NDoU9EZJawnXiIiJS7f+np4zdjSe6sijLH4TjqmKo1a8j5/IxU10xxdSIymayGwWVWg8usFoZNk7U1NfxLZZQFe/fx1zu34/3ud7l3YTOtF13MFZEKrvX7qHj5+l0REZkR9OouMsM9l0zyia5urvX5uMHvP+oYb2cH/o6DDK1aRX6K6xORsydkGFxnNbjO6sCMtbC5qYng9q28fVcrA20H+MziJbylsYkFLifnuN2scLs4x+1ihctNk8P+it16RUSkvCjsicxgiUKBt+xvo9pm428qI8e8cKtes4aCy8VIQ+MUVygiU8UwDOrcLlh5AR0LFlC5aRNf37SROw+2c895K/l50eSHw4nD4wMWCyvcLla43ZzjdrHE5aLF6aTSZlUIFBEpEwp7IjOUaZq8p+0gB7M5vlpfh+8YB6S7+vsJ7t3L8MqVZKe4RhEpjXQoTPtVVxNsb6fuhS3c/d9P8/dLlrLnskuJ2xzsy2bZm82yJ5Pl/sEhkkes4w1aLDQ7nbS4nDQ7HbQ4nYe/Dh7jdUZEREpDYU9khrpvcIhHhhO8uyLMUrfrmOOq166laLczMnfeFFYnIiVnGCTmzGGktpZoPE545w7O39VK3SWrWHb++ZjWQ2dtmqZJdz5PWzZHR+7QR3suxzNjYzw0lOfIrV6qbFaWuFwsdrlY7HKyxOVksctFrc2mbqCISAmcVNiLxWItwP1ABBgAbo/H47teNsYKfA24ATCBz8fj8e9M3Hc98DlgOXBPPB7/P5P2E4jIK+xMp/lAewfnu128ORw65jhHIkF45w5Gly8nY9HmvCKzkWm307dsGYmmJqq2vkD9//ye6Atb6LjyKhILFmAYBrV2O7V2+yu+N1ss0pHLHw6BbbkcbdksDwwOMfqybuDiieC31OXiAo+blR73MWcciIjI5DjZzt43gXvj8fgDsVjsNuBbwDUvG/N2YCHQzKFQuDEWi/0qHo/vB/YC7wbeCBy7xSAiZyxdLPLW/W04LAZ/X12F9TjvpletXweGwciCBVNYoYhMR1m/n4OrLsXb00Plls3M/6+fMNbYyMGrryZVWXXU73FYLMxzOpjnfOkuv6ZpMlAocCCboz2b5cBECPxJYoTvDQ4Bh85+WupycbHXw8UeNxd5PSx1uY77miUiIqfmhGEvFotVAecD103c9BDw9VgsVhmPx/uOGPpm4NvxeLwIV/4O5AAAIABJREFU9MVisR8DbwLujsfjuyce6+ZJrV5EXqJgmrxtfxubUmk+V1tN9DjbqdvGx4m8sJVkS4y03QE6d0tEgGR1NclrX0V4/z7CW7cS+8EPGFy+nM5LLyPv9Z7UYxiGQdRmI2qzsdLjfsl9Q/kCOzMZ4uk0OzMZHh0a5jsDgwB4LRZWut1c7PVwjd/HVT4vbs06EBE5bSfT2WsEOuLxeAEgHo8XYrFY58TtR4a9OcCBI75umxgzKbZu3TpZDyVHsX79+lKXIGfIBD5vsfEjDN5CkYqODuLHGb98506MQp7d4TCDbW1TVeZZ0d7eXuoSRM7IdHwOt9sd2JavYGF3NzVbtxLYsYPtC5uJz5tH8QynX1YAqyY+TKAXOGBYaCua7Esm+XIyyd29fTiBC4BLKbKqWGDOmf5QclboGkLK2Ux//pbNBi3Lli3D6XSWuowZaf369axcubLUZcgZ+lRXN49393JbOMRfRiqOO9aSyRD7718yPn8+vsZGvGXc1Wtvb6exUUdGSPma7s/h5Lx5tI+NUfnCFs7ZuYPFXZ10XHEFw80tMElTLhe97OtMscjmVJq1qXHWJFPcncuBxcICh4MbA35uDPi52u/Do65fyekaQspZOT1/M5nMaTW/TibstQP1sVjMOtHVswJ1E7cfqQ1oAtZOfP3yTp+InCXf6Ovnru5eXuP3866K8AnHR1/Ygi2ToX/RYswyDnoiMjWyPh8dqy7F29tLdMtm5j31FMnaWjquuopkXf2k/31Oi4WLvB4u8nr46yh05HKsTY6zZjzFdwYG+Xr/AE7D4Hq/j3dUhHltMKDpniIiR3HCsBePx3tjsdgm4K3AAxN/bnzZej2Ax4B3x2KxJzi0QcstwJWTXK+IvMxjQ8N84GAnl3k9/G1V9ITbmxv5PFXr15NqaCDp98MRO+aJiBxPsqqK5LXXEjrQRsW2rbQ8/DCJhc10XHEFmfCJ32g6XfV2O/WhILeEgmSLRTan06xNjvNMMsmTI6MELBbeFA5yWzjMlT4vFm3yIiICnPw0zvcC98disU8CQ8DtALFY7GfAJ+Px+DrgB8DFwItHMtwVj8f3Toy7HHgYCABGLBZ7C/CX8Xj86Un7SURmod+MjnHbgXaWu1z8wwl23nxRxfbt2JNJBi+7nKKCnoicKsPC8Ny5JBoaiOzeRXDHDhbv3UP/inPoWnUJBbfnrP71DouFCz0eLvR4eE80wqZUil+PJnloaJjvDgzRaLfz9ooQ7wiHWXKcM0ZFRGaDkwp78Xh8J4eC3Mtvv+mIzwvA+47x/c8CDadZo4gcxYbxcW7Zu58Gu51/qq3GdRJTmCzZLLV/+iPp6mqSoZB24BSR02babPQvWszQ3LlU7thJdPMmKrZvo/uii+k77zzMo5zLN9mshsFKj4eVHg8fKkb4Y3KcX42OcXdPH5/v6eN8t4vbKyr4i0iYoM70E5FZSBPcRcrQ7kyGG/fsw2ux8Pm6GvwneRFTvWYN9mSS4fNXUlDQE5FJUHC56T7vPNpefQOZqirqn/0fltz3PSq2b5vSaeIui4Vr/D4+V1fDo3Pn8MFohFTR5G86OqnfuoO/bu9gZzo9ZfWIiEwHCnsiZaY7l+PVu/eRNU2+UFdD5XHO0juSY3iYqvXrGGtpObRWT0RkEmX9fjpWXUrH6tXgcNL0i1+w6MEH8O/fP+W1VNhsvCEU5BuN9XyroZ6rvF6+PTDI4h2tvHr3Xn6aGKGoN7xEZBZQ2BMpI4lCgRv37KMrl+OztTXMcThO+nvrf/97MAyGly3TRY6InDXj0UoOrF5Nz6pV2NNpFj7xOAsf/yHu3t6S1NPicvKR6koeaZrD/6oIsymV4rV799OyPc5XevtIFAolqUtEZCoo7ImUiV3pDJe27mZrKs2na6pZ4jr5jQd8bW2Edu9i5LzzSFnL5nhNESlXhsFIQyP7r7uegfPOw9PdTeyBH9D0859jHxkpSUlhm5W3V4R5sGkOn6quwmux8LcdXdRv3cEH2jvYl8mWpC4RkbNJV30iZeCpxAi37W/DMODzdTWs9JzCbnfFIg3P/Jac309i7ryzV6SIyMuYViuDC5sZntNEZFcroXicUGucvvPOo+eiiymcwptWk8VmGFzt93G130c8neEniRH+fWCQb/YP8LZwiL+vqWJxCeoSETkb1NkTmcaKpslnunp43d791Njt/FtD/akFPQ4doO7u7ydx4YXofWsRKYWiw0Hf0mW03XAj401NVK1bx5L/+C6VG9ZjlHAaZczl5M7qSh5sauTWUJAfDidYuqOVW/cdYMP4eMnqEhGZLAp7ItNUolDglr37+XR3Dzf4fXy1vpaaU9zK3JpOU/uHP5Cur2ckWnmWKhUROTk5j4eulRfQfv315MJhGp55hsX330eoNV7So2AqbTbeG43w0Nw5vCMc4pcjo6yM7+bG3Xt5dixZsrpERM6UpnHKWdeWzfJCKk1/Ps9AocBAvsDA4c/z9E18nTNNmhwOFjgdzHc6WOBwsMDpZL7TQYPdflIHhs8U21Npbtm3n32ZLB+KRrglGMA4jZ+/5k9/wprJ0Hfe+TpqQUSmjUwwxMHLr8Db00N0yxbmPfUUydpaOq66imRdfcnqClqt3BGp4E2hEE8mEjyWGOGKXXu40uvl4zVVXO/3ndZrsYhIqSjsyVmxN5Phh8MJHhtOsG489ZL7rEDAaiVktRCwWAlbLTQ53Ngw6M7n+FNynCeGE+SP+B67YdBkt9PicnK518t1AR/nud0zMgA+MZzgnQfacRgG/1pfywq3+7QexzUwQOWmjYwtXkLS49EB6iIy7SSrq0m+6lpCbW2Et26l5eGHGW5upvPyK8iEwyWry2e18NaKMK8PBfnZyCiPDie4Yc8+LvG4uau2hlcp9IlImVDYk0nTmj4U8H44PMzG1KGDaxc7nbwnUsEKt4ug1UrQYsFrsZzwl2TBNOnL5+nI5enO5ejK5ejM59mZzvCzkVE+3gVhq5Vr/D6u8/t4ld/HAqdzKn7Ms6Zgmnyiq5t/7uljqcvJp2qqT/oMvVcwTeqf+S1Fu4OhxYsxFfREZLoyLAw3zSVR30Bk9y6CO3YQ3LOH/nPOpeuSSyic5htek8FlsfCGUJDXBQM8PTLKg0PDXL9nH5d7PdxVW8Nqv69ktYmInAyFPTkju9IZHh4e5tGhBFvThwLeMpeT90cquMLnPeU1Zi+yGgY1dvvE97/0F/1gPs+mVJr1qRTPjiV5fDgBwFyHnev8fl41EQDDpxuUpphpmvx8ZJS7unt4fjzF6wJ+PlAZxXEG7xoH9u0lcOAAQ6tWkbFoaa6ITH+mzUb/osUMzZ1L5Y4dRDdtpGL7drpWXUL/OediWq0lq81uGLw2GOD6gJ+fj4zw4NAw1+zey2qfl7tqa7jc5y1ZbSIix1MeV8My7Qzk83yqq4d/6x/ABFa4XXwwGuFyr5cq+9l9WlXYbFzj93GN34dpmrTncmwcT7E+leKhoWG+PTCIFbjM6+V1wQCvDfqJOZ3TbspNwTR5YjjB53p62ZRKU2Oz8fdVlVwf8J/R4xqFAvXPPEM2HGa4cY6mb4pIWSm43HSfdz7OBQuo3PICDc88Q+WmTXRceSWJBQuhhK/lDsPg5mCQG/1+nhoZ5T+Hhrli1x6u9/v4TG01l3gV+kRkelHYk1OSN02+2T/AJ7t6GCkUuCUY4C3h0OlPNzxDhmEwx+FgjsPBzaEgBdNkRzrD2vFx/pQc5yOdXXyks4uFDgevDQZ4XTDAFT4v9hJeLORMkwcHh/jnnj5aMxnm2O18rKqSa/y+SamrcuMGXMPD9F5/PXkFPREpU5lAkIOXXz6xictm5v/XfzHW0MDBq64mVV1d0tocE9M7bwr4+a/ECA8PJ1jVuoebAn7uqq0+5SNyRETOFoU9OWm/Ghnlbzo62ZbOcIHbzfujEeY5HaUu6yWshsEyt4tlbhd3RCroyeV5bjzJc8lx7u0f4Ct9/QQsFm4I+Hl1wM9lXg8tU9T1SxWLfG9gkH/p6eNALkez08Gna6q43OudtI1mXP391Pzxj4w3zWU0XAHF4qQ8rohIqSSrq0le+yrCB/YT3rqV2IMPMLRkKZ2XXUbOf2YzIc6Uy2Lhz8MhXhcM8OPECI8MDXNBfDdvCAa4q7aGpW4dzi4ipaWwJye0J5Phwwe7+MnICPV2G/9YU81lXs+0mxZ5NNV2GzcHg9wcDJIqFlk/nuL58XF+PTrGoxNr/SJWK5d6PVzq9XKpz8MFHg+eSVjnZpomnbk8m1OH/s5v9Q/Sk8+z3OXi89EIF3nck/rf0JLJMO/J/8J0OBhceT5FBT0RmSksFobmzSfR0EC0tZXwzh2EWuP0XnAhPRdeSPE014dPFrfFwlvDIf4sGODx4QSPDif4UaKVt4VDfLq2moVlvoGYiJQvhT05ptFCgc/19PKl3n6swF9VhHljKIijTDf8cFssXO7zcrnPS3Fird/2dJpt6QxbUmmeHBkFDv2jONfj5jKvl/PdbiI2K0GrlZDVStBqIWi14rNYsBwR1LLFItvTGTanUmxOpQ//OVAoHB5zkcfNx6orOcflmvygbJrM+eUvcQ4P03vDDaSs+qctIjNP0e6gd+myQ5u4bNtGzXN/IvLCC3RefjmDS5aUdD0fgNdi4faKMDcHAzw6NMzjwwkeHhrmjkgFn6ypotExvWbDiMjMpytCOarfjo7x9v1tdOXz3OD38ZeRCqJlsrvlybAYBk0OB00OBzcGDt2WKBTYnk6zPZ1hWzrNt/oHSB9jzZsBBCfOCXRaDPZnc+QmxjoNgwUOB5d6Pcx3HDogfr7Dic969kJy5YYNhHe1MnTxxYz6A9qURURmtJzXR+dFF+NesJDKLZtpevoXVG3cwMGrrmassbHU5RG0Wnl3NMIbQ0H+c2iY+weH+P7gEO+NVvDx6iqqS9yJFJHZY+ZcvcukME2TL/b28bHObuY47NzbUMcS1+xYcxC0Wlnl9bJqYje1vGnSlcsxViySfPGjcMTnZpGxQpGMaXKR28N8p4MFTgcNdvuUHvbu7ThI/f/8nuT8+Qw1zdWZeiIya6QiEdquXk3g4EEiL2yh+bFHSSxcyMErriRbwkPZX1Rhs/GByii3hoI8ODTMvX0DfKd/kA9WRvlIdSWRGfQmqohMT3qVkcNGCgXuONDOE4kRrvZ5+UhV5aSsXStXNsOY9lNubMkk8556ipw/QP/5Kyko6InIbGMYjDQ2MlpXS2T3HoI7trNk7330nXsu3ReX9lD2F9XY7Xy4qpI3h0J8f2iIf+nt497+Af6mMsrfVUXL5lxYESk/s/dKXl5iRzrNxfHd/CQxwvsjFXyyumpWB72yUCwy92c/xZrJMHDllWTLYMMcEZGzxbTa6I/F2H/DDYzNm0flxo0s/Y/vUr3meYxcrtTlAdDgsPPx6iq+29jAhR43/9TTy7xtO7mrq4fEEWu8RUQmi67mhV8ZFi6K76Y3n+eLdbW8KRwqi502Z7vaP/wBf3s7Q5ddTnKWTLUVETmRgstN9/krab/+ejKVldQ9+yxLv/cfVGx9YdocRzPP6eCTNdV8u7Gec9xuPtXdw7xtO/nn7l7GFPpEZBIp7M1iedPkIx2dfMywMtdh55uN9ZzrKf10FzmxwJ7d1Kxdw+iSJQzX1KDJmyIiL5UJBOlYdSkdq6+h6HLR9MtfsvgH3yewd8+02cRqodPJZ2qr+WZDPYudTj7e1c28bTu5u6eX8WkSTEWkvCnszVI9uRzX7d7LF3v7uQaTL9XXUak1A2XBMTxM0y9+QaaqisFly3WenojIcYxHo7RdvZqeSy/Dmsux4Mc/pvnRR/F0dZW6tMNiLiefravh3oY65jsd3NnZzfxtO/liT586fSJyRhT2ZqF14+Ocv3MXzyXH+XhVJX9eLGDXtM2y8OLB6QbQf/nlZKfJu9MiItOaYTBSX8/+666nf+UFuAcHiD30n8z/8Y/wdHeXurrDlrhcfKGulq/V19Fot/ORzi7mbtvJ57p7taZPRE6Lwt4s8/TIKFfv2gvAPQ11XBfwl7giOVm2sTGaH30E98AAg1ddxbgOThcROTUWC0Pz57Pv1TcwtHwFvo4OYv/5IAueeGJadfqWu13cXV/L1+vraHE6+b9d3czdtoNPdXUzmM+XujwRKSMKe7PIg4NDvHbPPursNu5pqGOh01nqkuQkOQcHaHn4IVzDw/Rddx2JYKjUJYmIlC3Tbqd/0SL233gTQyvOwdvdReyh/2Th44/j7ewodXmHLXW7+FxdDd9sqGeFy81d3b3M3baTv+/soi+n0CciJ6awN0v8a08ftx1o5xy3iy/V1VGh9Xllw9PZScvDD2PN5+l59atJBILakEVEZBIU7Xb6YzH23Xgjg+eci6enm5aHH2bhDx/D23Gw1OUdFnMd2sjlu40NXOTx8IWePuZu28HfHeykIzs9jpUQkelJV/wzXNE0ubOzi3/t7We1z8vHqqtwaH1e2Qjs2c28n/6UvNdH3zWrGbfZS12SiMiMY9rsDLS0MDh/PhX79hGM76TlkUdI1tbSd+55DLe0YFqtpS6T+U4H/1BTxe3ZEA8NDfO1vn6+3j/A28Mh/k9VJUvdOoZHRF5KYW8Gy5km7zrQzgNDw7whGOD90QhWBb2yEdmyhcZf/4pMVRV9V1xB2lAjXkTkbDJtNgaamxmcN4/wgQMEdu9m7s9/Ru53z9C/YgX9K84h7/OVukzmOBx8tLqK2yvCPD6c4JGhYe4bHOI1AT8fqarkSp9X5+WKCKCwN2ONFQrcuu8AT4+O8e6KMG/VQenlwzSp+dMfqX3uOcabmui7+BLtuikiMoVMm43BBQsYnD8fX18foT17qHnuOWrWrCGxcCG9555Hsr4eSvx7tdZu5wOVUd5REebJxAhPJEa4evdeLvC4+WhVJa8PBfUmr8gsp7A3A/Xl8ty0dx8bxlPcWRXlxkCg1CXJySoWafzVr4hufYGxxYvpW76CvIKeiEhpGAZjVVWMVVXhSK4gtG8v/r17CbW2kopW0r9iOcPNLeS93pKWGbRaua0izJtCQX45OsZjwwnetL+NBQ4Hf1cV5S8iFXgsmh0iMhsp7M0w+zJZrt+zl/Zsjn+srebSEv8CkpPnP3CA+meewT3QT+L8lQwsXEhBQU9EZFrIer30LltO36LFhDo6COzZTeNvfkPDb3/LWEMDQy0xEs3N5D2ektXotFh4XTDATQE/f0gmeWQ4wV8f7OQTXT28O1LB+ysjzHE4SlafiEw9hb0ZZGsqzfW795IsFrm7rpblWqhdFhxDQ9T//neE9uwhFwjQf+21JCJRigp6IiLTjmmzMdTUxFBTE67RUfwdB/G2tzPn17/C/M2vGWtoZCjWQmJh6YKf1TC40ufjCq+XLek0P06McHdvH1/s7eP1oSAfrIxwhVfr+kRmA4W9GeK5ZJKb9uzHCnylvo55Tr1zN91ZMhlqnn+Oyg0bwGpl+KKLSMydd2h9noKeiMi0l/b7SS9aTF9s0UTw68Db3sacX/0K89e/ZqyhgdE5cxhraGS8pmbKd/Q0DINz3G7OcbvpzuV4KjHCkyOj/HA4wbluFx+qjPLWcAiXpniKzFgKezPAf4+M8vp9BwhbrfxLXQ21dm3PP60Vi1Rs20bdH57FPj7O2KJFDC9ZQspqU8gTESlHhkE6ECAdCNC3aBHukRH8nR24Ozvx/+EPABRtNpJ1dYw2NpYk/NXY7fyvaITbKsL8ZnSMJxIjvKvtIHd2dPFX0Qjvi1bQoCmeIjOOwl6Z++HQMG870E6T3c4X6mp0WPp0VigQOLCf2j/+EU9vL+naWgZWX8OY14upkCciMjMYBqlgkFQwCIuXYMtm8Q4M4B7ox9nT+9LwV1vHeFUV6cooqWiUdEUE8yz/HndZLNwUDHBjwM/m1KEpnp/v6eULPb28LhjgvdEI1/l9WDTFU2RGUDIoY9/pH+A97R0sdbn4bG01/mlw4Ku8jGni7ewkvHMHodZW7KkUOZ+P/tWrGamsOrQBi4KeiMiMlXc4SNTWkqithWUcCn+DA7j6B3D19eLbtBGjUADANAwy4TCp6ET4i0bJhMJkAwGKk9x1MwyDcz1uzvW8OMVzlJ+NjvLjxAhzHXbeG41wR0UFVXZdKoqUM/0LLlN39/RyZ2c3l3jcfLKmGrfm208rroEBwjt2EN65A+fICEWrldS8eQzNaSIZiZAHhTwRkVko73CQqKklUVMLgGGauJJJnCMjOEZHsCcS+Lq7Cbe2vvT7XC6ygcArP3x+8h4PeY/ntLuCh6Z4VvDOSJhnx5I8NTLCxzq7+URXD2+c6PbpoHaR8qSwV2ZM0+TjXd18vqePa31ePlpdhV0vviVnyWbx9PTg7ewg1NqKp68P0zBINzYycM65jFVXkzMMFO9ERORIpmGQ8vlI+XxA3eHbrYUCrtER7OPj2FIpbOPj2JJJvAMDBPbvx5LPv+KxCg4HeY+H3ET4y3s85N0e8m4XeZebgstF3uWi4J740+mCI94sthsGq/0+Vvt9tGWzPJUY4acjozw8nGCR08l7oxHeURHSkhGRMqJ/rWWkYJq8v72Dfx8Y5OaAnw9WRrEq6E0908Q5OIi3uwtvVxeeri7c/f0YE526THU1g5esYqy2lqzdrvV4IiJyygpWK8lQGELhV9xnALZcDkcqhS2VwprNYM1msabTWDIZbOk0jsFBrB0dWFIpjnelUHA6ybsmwuBEKMy7XdS43JzvdnOn08k6w8KT2QyfHBvjox2dvCEc4t2RCq7y6SxfkelOYa9MpItFbj/QzmPDCd4RDnFHRVjTKc4yI5/HkUjgHB7GmRjGOTSMc2gQb08P1kwGOPQuara6mpHzziMdDpMJhsjZbP//jDwFPRERmWQmkLPbydntEAgcc5xhGBimiTWXm/jIHgqFuRyWXA5LNoclm8GayWLJZrAnkxiDg1hTKSy53OHHaQbeOvF5xmql2+mky+niRx4PXoedvmSSUCSCpaICSziM4dI5vyLThcJeGejP57ll737+kBzn/ZEK3hQOlbqk8maaWNNpbOPjE9Njxg9Njxkfxz42hnM4gXN4CPvY2EveDS04HOSDQcbnzycbiZAOhcl4vRSLxZdOz1TAExGRacA0TUyg+GIw5MSHvBtMhMRiEVsuhyWbxZ5OY81ksGQPdQ3DqRS2ZJL8yAjBdArHvn2MH/kYbvfh4GcJh7FUVGCtrsZSVYWhzeREppTC3jS3K53hpj37aMvl+HRNFVf5fKUuaWqZJv+vvTuPsuMq7Dz+vbW8rben3tWSrMWSrgJ2rLFlGxIbAwePmYkTw8HgYzAWycAkDMlhyDADTMLAJCRDAkwyYRmYCSE+QDwhCQMBzOYwBIvDYowXeaEkGa2t1tp791ur7vxRrxfJstSypd70+5xTqqpb9dT3dZda9Xv31r2mXsevVvGqVbx6/ZTFTG/X0vMa/zFNnT+zruHVqgSNkGfOEMicMcT5PPXWVqp9fUw2N1NvaqJWKFBraqIehCRwarfMJJm/74WIiMhF5mj8P2cMcSYDmUzjecIzu2/gCI+tWMETY6OsGJ/gqtIkL69UeEG5RO7AAdwTT8x8COp5eF1d+CtX4vf04Pf24vf2qiVQ5CJS2FvEdoxPcNvP9+GAj/St5Ir8Mvhl2GhVC8fHyYyNEY6NkRlP1+HEZNqdpFrDr1bwGuszBbOzfgnPI8lkSMIQF4a4qe1CnkpnF5O5HHE2S5zNkmQyxJkMtWyWOJPBGYM7vaVuVt1FRERkRm+9xrW5LPVsJzvaO/hykvBHiSMBXhr4/EYYcFulTGZ4mGR4mGRwkPquXdQeeWT67zDFIsGaNQRr1+KvXYvX0aFHVUQuEIW9RerewSHedOAQvUHAH/f1sioMF7pK580vTdJ05CiFI0coHD1CdmiIcGwM/7QRxJwxxE1NxIVCGsxac1TDdlwYkgQBSRiSBCEu8NMg56dr5/sknt9Ye7ggwAUBiTE4Y6DRfWXOA6RozjsREZHnJDCGl/qGl/oeR53jvjjhK3HM3fWYNgx3dnXz66tWc3WQduNMymWSwcGZAPj009R27gTANDVNB79g7dq0+6fCn8hzorC3yDjn+G9Hj/N7A0fYms/xX3t7aF0C/dunph6YCnaFI0fIjo4CjQfJ29upr2hnYtVq4sLUMNDpqF+1bHY6lF2QqKXQJiIismB6jOHXA5/tzuOnzvGVOOEzlRqfrNS4yvd4UybD6zNZ2vv6oC+dbsI5hxsfJz52jOTYMeoHDlB78kkgfQbQX7+ecONGgo0b8VpaFvLtiSwpCnuLSM053nrwEJ8+OcTNLc28s7uLzCL+JCsYH6f49B7a9uyh5eBBTOP5tVprK7WuLoa2bKFSLFJpbaPueTMjVJ5OwUxERGTZ8YxhmzFs8zze6RzfTBL+MXa8o1Tm3aUyrwoD3pTN8PLAxzMG09KSBrnLL0/D3+TkdPiLDxyg3gh/3sqVhJs2EWzahN/Xh5k1V6CInEphb5EYiWNu//l+7h8fZ/uKItsX6dQKmaEhinv2UNyzm6aBAQCqxSJjV15JqbOTSlsbtTBDcvrAJQp0IiIil6wWY7jd97ndhyhxfDVJ+Hqtzt/W6qwxhjdlQ+7OZFjnp8HNGINpasJbvx7Wr8dddx3JyAjx4cPEhw5ReeABKt/7HiafJ9i4kWDTJsJNmzTYi8hpFPYWgd3lCq/eu4+oXOHd3V3c0rq4uidkhodpf/IJirv3kD95AoBKVxfD27Yx2dtLudBErBEqRUREZA6sZ7Cez2/7Ht9LHF9JEj5QrvKBcpWXBz5vymZ4VRiQm/WhtzEGv1jELxbhBS/AVavEAwPEhw9T37OH2s6dlDyPYP16gi1bCK1Vd08RFPYW3OcGh3jrwX584E/6VnJ1Ib/QVUolCa1799L16CO07NsHxlBZ2cfQi17MeE8P1VxupvVOrXYiIiJynrJTCQGtAAAWWklEQVTGcLNvuNn3OOIcX40TvhrHvHGiRBG4MxuyPZPhat97Rm8nk8kQNAZwcddfT3LyJPGhQ9T376f+ta9R/trX8FevJtyyhWDLFvyOjoV5kyILTGFvgUzECb99qJ+/Hhxiaz7Hf+7ppitY+B9HMDFBx+M76XzsMTJjY9Sbmhi55hrGL1tLJQxnnrtT652IiIhcIL3G8ObA5zecx0ONQV3+qlLjf1ZqXOl5bM+GvD4T0nWG5/OM5+F3deF3dRFu3YobHaV+8CDxgQOU778f7r8fr6trJvitXLkoH5URuRgWPl1cgh4rlbhj7wGiSoXtK4q8sX0F/kL+0nGO5oMH6XzsUdp278ZLEkqrV3N827WMd3VRnwp4asETERGRi8gzhmuN4VrP4z86x7eShK/GjneWKrynVOHWMGB7JuSWMCA4w72TMQbT1kamrQ2uuIJkYiJt8TtwgMqOHVQeeADT2kpoLeGWLfhr12KWwKjnIs+Vwt48cs7xqZOD/PtDh2nxPD7St5J/sZDdNpOEFVHELd/fwYrRUeJslvEXXsHYunWU8/m0FU8BT0RERBZAizG8xvd5jQ97EsfXkoT7anX+b61OjzG8MROyPRuy5SxhzWtqwrOW0FpcpUK9v5/40CGqDz9M9cEHIZcj3LyZ0FqCyy/HZLPz+A5FLj6FvXkyXI95y8FD/P3wCNcX8ryru5sVwcJ8kmRqNToef5zuh35CdnSUiZYWTt5wA+Mr+6hOnaSQJyIiIovERs/wds/nbb7H9xujef5ZpcqHK1Wu9T3uymS4IxPQcZZpGEw2S7hhA+GGDbh6nfjIkbTVb9cuao89Bp6Hf9ll6Xx+mzbhdXWpu6cseQp78+BHE5PcsW8//dUab+1o5/ZiG94C/PLwSyW6HnmYrocfJiiXKff2cnzbtUTVKj0rV857fURERETOR2AMN/mGm3yPE87xjTjhvsTx9lKZd5bgV8KAuzIh/yoMzjpXsQkCgtWrCVavTqd1OHEindahv3/6OT/T2jod/IL169XqJ0uSwt5FNB7H/PHRY3zo6HG6g4D/sbqPFyzA/C/h6CjdD/2Ejp078et1Jtet4+SWX2CirY04SagePDjvdRIRERF5PjqN4a7A5y5gV+K4L0n4Rq3Ol2p1OgzckQm5K5Nh2xlG85zNeB5+dzd+dzds3UoyOZkGv4EBqo8/TvWnP01b/VavJli7Fn/tWoI1azCZzPy9WZHnSGHvIkic4/NDw7yrf4CBep1XtjTzts4Omuf5AeD88WN0P/gTVkQ/A2OY2LSZsc2bmMwX0ufxNKKmiIiILAObPcPmxtx9P2oEv7+s1PhEpYb1PO7MhLw2E7B5DvdiXqGAt3Ej4caNuDhOW/0GBoiPHKGyYwc88AAYg9/Xh3/ZZekUEJddhskvkumzRGZR2LvAfjwxydsP9fPDyRIvzGX5/d7u+W3Na4ys2fPgg7Tu30cShoxd+YuMbthAOZPBadAVERERWaYCY/hl3/DLvseYc3wncdwXJ7y/XOH95QpbfY87MiG3hyHr/Gd/vm+K8X38nh78nh6A9Fm/EydIjh0jPnqU6o9/TPUHPwDA6+7GX7lyZuntVeufLDiFvQtkoFbjPYePcM/gEJ2+z3u6u3hFS/P8PZuXJBT37Kb7wQdpOnqUeqHA8HXXMbbmMspTDysr5ImIiMglosUYbvMNt/keR53jO3HCtxLHexrTOFzve7wuE3J7JqTvLAO7zGaCgKC3F3p7AdKWv8FB4mPHSI4fp757N7VHH50+3+vsPCX8ed3deE1NF+X9ipyJwt7zVEkS/vz4CT5w5BgV53hDscjr24sU5vhL4/nyqlXan3wyHVlzZIRqsZiOrNm3iqrCnYiIiAg9xnBn4HMncMg5/ilOuD9x/IdShXeWKtzo+7w6E3JrGMypxW+K8f3pCd0hnWbLVSokJ0+SDA2RDA5S37uX2s6dM6/J5/G6utIg2NmJ19WF39mJaWvT6J9ywSnsPUfVJOHvh0f4LwNHebpa5camAr/Z2cGqMJyXr587cYLORx+h/amn8KtVyj09HL/mGsY7G5OgK+iJiIiIPMNqY9ge+GwH9iWO+5OEbycx7yjFvKMEV3get2YCfiUMuM73z6uXljEGk8vhrVoFq1ZNl7tymXhoCDc2RjIyQjI8TP2pp6iVSjMvDkO8jg689nb8FSvw2tun901zs4KgPCcKe+epv1rjf508yadODHK0XmdDJsOH+3q5plC46F/b1OsUd++m87FHae7vJ/F9Ji/fyNjlGyi1tBIr5ImIiIjM2TrP8GbP58347E8cO5KEHYnjQ+UqHyxX6TKGfx0G3BoGvCIMaH6OgcvkcgQrV8JpU125ajUNf6OjuNFRktFR4sOHqf/sZ6cOpBeGeFMBsL0dv719et+0tmLmqUeZLD0Ke3PgnOOBiQk+dvwkXxweIQFeXCjwu12dbCvkL/pzeZmRETofe5T2xx8nLJWotbUxfP31jK5aTdX3cWklL2odRERERJaztZ5hrefzBmDUOX6QOB5IEr5YrXFPtUYWeEngc1MQ8JLQZ5vvEz7Pe0CTyZzSDXSKcw43OUkyNoYbG8ONj5OMjpIcO0Z9926I45mTfR+vWJwOglMtgn57e9o1VEHwkqawdxbjccznh4b56PETPFGu0Op5vLbYxq+2tdJ3kbtrhmNjFHfvprh7F039/WAMpXXrGNq4ifH2dmJNmyAiIiJyUbQawy2+4Rbfo+4cjzjHjsTx4zjh2/UKlKEJ+KXA5yWzwt/ZJnI/H8YYTFNTOphLYzCYKc45XKmUdgmdmEjXY2Np19B9+6BWmznZ99MWwKnuoR0d6XZHh7qGXiIU9k4zmSR8d2ycr46O8fnBIUaThM3ZDP+pu5OXNTeTu4ifjoSjo9MBr/nwYQAqHR2MbNvG+Jo1lMPG1AkKeiIiIiLzIjCGbcawrXELOOQcDyeOh53joVnhrwC8OPC5MQi4OvDY6vusvAj3jcYYTKEAhQKnzxroSJ8PdKOjJOPjaRAcHSU5cYL6nj2ntghmMukAMbMCoN/ZmXYN1ZQRy8YlH/acc0SVCl8fHeMbo2P88/gEFefIGcONTU38WlsrL8xlL84nH86RO3mS1r170xa8I0cAqHR1MXzttUz09lIuNJFMhTt11RQRERFZUCuM4eW+4eWN/eHTwt/765Xpc7uNYavvsTXw2er7XOV7bPS8i/YIkCF9PpBcDr+7+5RjDnCTk2kQbHQPTUZGqO/bh5s1WiiAaW1Nw9+sIOh1dOAVi+oWusRckmFvLI75ztg43xgb4+ujY+yvps3d6zIht7W2sq0pz1W5HJkLfTHHMYVjx2juP0RTfz/N/f0E5TIAle5uhq+7jonelZTz+ZmAp1Y8ERERkUWraAwv8w0va+yPO8cu59idOHbhiOKE79Rj6o3jzcCVvscm32eD57HOM6z3PdZ7Hr3GXLSulQZmWgRP7xoax2kAnGoRHB0lGRmhunMnVGbCK56XPhM4NVjMrEFjvGIR45/e1igLbVmHvcQ59ldrPFYqsbNcZmepzGOlMrsrFWKg4Bmuyed5TVsb1xby9F7I5/CcIxwbI3/yJIWBwzT399M0MIBXT/+pV4tFyuvWUensYrKjg2oup4AnIiIissQ1G8PVxnD1rDaDqnPsdRA5x27n2JU4vhHXOHbaa3PAOi8Nfut9Q5/x6PAMnSZdprbbjcG/gKHQ+D5+sQjF4inlzjmo1dKuoKOjMwPFDA098/lAYzBtbWkAbGtLl2IRr60tLW9txQRp9Eico+YcdQcxjviUbag3ymKXtkies/6k3W19Az6GwIBvDP50uSE0hgAuuecU5xT2rLWbgXuADuAkcHcURbtPO8cH/gJ4JenP5YNRFP3luY49X7FzHK/XOVyrcbhWZ1+12gh1JR4vVxifFZxWhQEbMhmuLxS5Kp/ninzu+Y+iVKuRGx4iOzhIrrFkBwfJDQ1NBztnDNXOTia2bKHc2UW5WKSazc6EO1DAExEREVmmMsZgDVhOve+sOMeAg37nOMzM9t445oG6Y/xZ/j4DFA10GkPRGPLGkAdys9cGcsysTeN1AFO3v6eUATFQb4St2vQ21AvN1AtN1B3UgCrpnNP5SoX28XE6J8bpGp+gZ2KcnpERugcG6Gr0XpvtaDbLoXyBgVyOo9kcx3LZdJ3NcjSX41hju3KRWggN6c8iawwZY/C8kKYnniJrPDLGkPMMec8jbzzyU9ueaezPbBc8jybfo2AMBa+x31ini5nez1zE1tq5mGvL3ieBj0dR9Dlr7V3Ap2C6q/KUNwAbgU2kofBha+39URTtO8exObl3aIjDeByu1eiv1abXR2p14tPObfM8Ls9meGVLM+syGdZnM6zLZCicq1tmkuDVavjVKl61il+t4lcqhJMTBBMThBOT6Xr2fmXmQnZAvbWVenEF47291FpaqTUVqLQVqXseyexn7hTuRERERC5pWWNYZ2AdZw4DZecYAYYdDOMYmbUeIR0sZtw5JhPHIFABKjgqbmobysytdex0fmMJZ237QGjSABECYRASFleki2mUkR7PJwk95RLdpRI9kyU6yiU6JydpL02yqlyiZWiQptldRGephCHlbHZ6qWSzlLJZytkclUyWcjZDNQyphSHVIKSSCaj6IaUwoByElMOQmjEkpK2IaYh11BstijUcNQeDI6Pkstm0zDmqzjESxxxP6lScaywJlcRN7z+X72PBS8Nik2emg+FUGMxPrRvBMd8Ii7PL855HU1yn95xf7ZnOGfastd3A1cDNjaJ7gY9Za7uiKDo+69Q7gP8dRVECHLfWfgl4LfChcxw7Fx/gz/uPcMwYmj2PTt+n18BbDuynq1ZNP7WAxicZkAWMcxgHJokxcYxJEkycpNsuwdRjTBLj1WO8WhWvVptuiXs2SRCQ5PMk+Tz1ri4qa3Ik2Sy1QoE4X6CWzxOfHuogHVgljp/ln/HCywQBRuFTlihdv7LU6RqWpUzX78U1dX87fZNvGn+YUwrOzjnqzAQ+x5nDnwM8ZkIdz7c1yvcgbIGWlmccGgcmjME4R9BoYAkqFbxqJW10qdUw1SqFWo3mahWvVMIMj+BVK3O+3pwxON8n8X3wAxLfwwUBiefhfB/n+5QrFbKFAngezvPS13gezvPBM+n3yaTlGIMzhthAbEza7dTQ6H6atngmTLWKmrRF1DW6qMLMfmNdMYZ/6lvFgUyWqktmwmXiONM77HaOTze+s+fzY5hLy94aoD+KohggiqLYWnu4UT477F0G7J+1f6BxzrmOnctKgE8Tp1dh3FgAerqfcXK1sSyI6pk/nVjseottUHlmU7vIUqDrV5Y6XcOylOn6XRoW4yAdU6GzChAG6ULTvNfj2brKzoVhpjUz9xxefyMJVEvn+7KVwNNzPXkx/uxP9yBwIzAAz+ixKSIiIiIistz5pEHvwfN50VzC3kFglbXWb7Tq+UBfo3y2A8DaWRWY3Zp3tmNndc0111SAHXM5V0REREREZJmac4velHNOJBdF0THgEeDORtGdwMOnPa8H8HfAW6y1nrW2C3gV8A9zOCYiIiIiIiIX2FxnDf8t4HestbuA32nsY629z1q7rXHOZ4GfA7uBHwJ/EEXRz+dwTERERERERC4w457DEKIiIiIiIiKyuM21ZU9ERERERESWEIU9ERERERGRZUhhT0REREREZBlS2BMREREREVmGFPZERERERESWoblMqi7LlLV2M3AP0AGcBO6Oomj3wtZK5MystR2k07hcDlSAPcBvRlF03Fr7IuBTQB7YB9zVmCNUZNGx1r4PeD9wZRRFj+v6laXCWpsD/gx4BVAGfhBF0b/V/YQsBdbaW4E/BAxpg9f7oyj64nK/ftWyd2n7JPDxKIo2Ax8nvdkQWawc8KdRFNkoin4ReBr4oLXWAJ8D3ta4lr8HfHAB6ynyrKy1VwMvAg409nX9ylLyp6Qhb3MURVcC722U635CFrXG79rPAm+MomgrcBdwj7XWY5lfvwp7lyhrbTdwNXBvo+he4GprbdfC1Urk2UVRNBhF0XdnFf0QWAtsA8pRFO1olH8SeN08V0/knKy1WdIbiX9H+uEF6PqVJcJa2wzcDbw3iiIHEEXRUd1PyBKSAG2N7SIwAHSyzK9fhb1L1xqgP4qiGKCxPtwoF1nUGp/EvRX4R+AyYP/UsSiKTgCetbZ9gaon8mz+APhcFEV7Z5Xp+pWl4nLSLm7vs9b+xFr7XWvtDeh+QpaAxgcUrwO+bK3dD3wJ2M4lcP0q7InIUvRRYBz42EJXRGQurLUvBq4FPrHQdRF5jgJgA/BwFEXbgHcBXwSaF7RWInNgrQ2A9wC3RVG0FvhV4G+5BK5fhb1L10FglbXWB2is+xrlIouWtfbDwCbgjiiKEtJnn9bOOt4JuCiKBheoiiJnchOwBdhrrd0HrAa+CWxE168sDfuBOo3ublEU/Qg4AZTQ/YQsfluBviiKvg/QWE+QPoO6rK9fhb1LVGOkt0eAOxtFd5J+Wnd84WolcnbW2j8CrgFeFUVRpVH8EJBvdCcC+C3gCwtRP5FnE0XRB6Mo6ouiaF0UReuAQ8AtwIfQ9StLQKOL8f8DbobpEb27gV3ofkIWv0PAamutBbDW/gLQC+xmmV+/xjl37rNkWbLWbiEdanYFMEQ61Gy0sLUSOTNr7QuBx0lvLEqN4r1RFL3aWvtLpKNn5ZgZuv7oglRUZA4arXu3NqZe0PUrS4K1dgPwV6RD1NeA34ui6Ou6n5ClwFr7BuDdpAO1ALwviqIvLffrV2FPRERERERkGVI3ThERERERkWVIYU9ERERERGQZUtgTERERERFZhhT2REREREREliGFPRERERERkWVIYU9ERERERGQZCha6AiIiIgvNWvtd4CqgN4qiygJXR0RE5IJQy56IiFzSrLXrgBsBB/zawtZGRETkwlHLnoiIXOruBn4I/AjYDvwdgLW2A/hr4CYgAr4JvDSKohsax7cAHwWuAY4D742i6AvzXXkREZFno5Y9ERG51N0NfL6x3GKt7WmUfxyYAHpJQ+D2qRdYa5uAbwN/A3QDdwKfsNa+cB7rLSIiclYKeyIicsmy1t4ArAW+EEXRQ8DTwOuttT7wGuB9URRNRlH0JHDPrJfeCuyLougzURTVoyj6KfAPwO3z/BZERESelbpxiojIpWw78K0oik409v+mUXYv6f+RB2edO3t7LXC9tXZ4VlkAfPYi1lVEROS8KOyJiMglyVqbB14H+NbaI43iLFAEeoA6sBrY1Ti2ZtbLDwL/HEXRzfNUXRERkfNmnHMLXQcREZF5Z629k/S5vK1AddahLwAPkga9GHgzcBnwLeBAFEU3WGtbgMeB3wf+T+N1W4HxKIqemp93ICIicnZq2RMRkUvVduAzURQdmF1orf0Y8BfAlaSjcR4hHY3zXmAbQBRFY9bafwn898biAY8CvztflRcRETkXteyJiIjMgbX2T0gnXd9+zpNFREQWAbXsiYiInEFjHr0MsBO4Fvg3pF06RURElgSFPRERkTNrIe262QccAz4CfHlBayQiInIe1I1TRERERERkGdKk6iIiIiIiIsuQwp6IiIiIiMgypLAnIiIiIiKyDCnsiYiIiIiILEMKeyIiIiIiIsvQ/wcux84RlqjCcwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "ax = sns.kdeplot(final[\"age\"][final.survived == 1], color=\"darkturquoise\", shade=True)\n",
    "sns.kdeplot(final[\"age\"][final.survived == 0], color=\"lightcoral\", shade=True)\n",
    "plt.legend(['Survived', 'Died'])\n",
    "plt.title('Density Plot of Age for Surviving Population and Deceased Population')\n",
    "ax.set(xlabel='Age')\n",
    "plt.xlim(-10,85)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "8e304d72-27f3-41cf-863f-63872f4c37df",
    "_uuid": "6c5625b454f5e01dd6b6d843d801851c14c64d1e"
   },
   "source": [
    "### 生还与遇难群体的分布相似, 唯一大的区别是生还群体中用一部分低年龄的乘客. 说明当时的人预先保留了孩子的生还机会.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "a643b196-91c6-4b12-9463-0f984fbfc91a",
    "_uuid": "337b3ced0c6423cf1d126f23a7e60c0181af6a47"
   },
   "source": [
    "<a id=\"t3.2.\"></a>\n",
    "## 3.2. 票价"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_cell_guid": "9f31ffe1-7cd8-4169-b193-ed44e56d0bd4",
    "_uuid": "4a1c521f08460f6983eca0c4e01294fb7c86e4f9"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,8))\n",
    "ax = sns.kdeplot(final[\"fare\"][final.survived == 1], color=\"darkturquoise\", shade=True)\n",
    "sns.kdeplot(final[\"fare\"][final.survived == 0], color=\"lightcoral\", shade=True)\n",
    "plt.legend(['Survived', 'Died'])\n",
    "plt.title('Density Plot of Fare for Surviving Population and Deceased Population')\n",
    "ax.set(xlabel='Fare')\n",
    "plt.xlim(-20,200)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "346b7322-a3e4-48df-bbb1-4d8ec7716f3f",
    "_uuid": "2717310b6c443d675c7342be0c2c18b265723273"
   },
   "source": [
    "生还与遇难群体的票价分布差异比较大, 说明这个特征对预测乘客是否生还非常重要. 票价和仓位相关, 也许是仓位影响了逃生的效果, 我们接下来看仓位的分析."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "cf585311-4029-4be4-8af2-3eea8258801a",
    "_uuid": "4524affda51265ea23fa923e2ea7f93d7bb91875"
   },
   "source": [
    "<a id=\"t3.3.\"></a>\n",
    "## 3.3. 仓位"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_cell_guid": "676548e8-6dd4-4180-800c-7b164acb3877",
    "_uuid": "08fd677214959e0b938a0f8a94b63ab548673ea5"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot('pclass', 'survived', data=df, color=\"darkturquoise\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "193233f8-b220-4cae-aa0f-f822316d5623",
    "_uuid": "8ddb19191253a6e09dfcb0beff2b3690f1052d52"
   },
   "source": [
    "如我们所料, 一等舱的乘客生还几率最高."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c59f8e8f-e8c2-40fb-b9c8-12dddd6d318f",
    "_uuid": "2fc06b75321946b721852f78431435f9ba5fef39"
   },
   "source": [
    "<a id=\"t3.4.\"></a>\n",
    "## 3.4. 登船地点"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_cell_guid": "6e5bec50-2f5e-433e-9130-c56956fddad3",
    "_uuid": "a9f0598701c7c5224eaa73dafa869af73beffe18"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot('embarked', 'survived', data=df, color=\"teal\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "88d78820-35a5-48fd-a234-9f3ca3fca779",
    "_uuid": "2f6a0329cf0c7b771a707ec790efc065924e1ee2"
   },
   "source": [
    "从法国 Cherbourge 登录的乘客生还率最高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "9e6dc87e-ba59-4004-8145-79709328fe27",
    "_uuid": "92bacce85a7dec5509217b9570bc2a2fea6a8452"
   },
   "source": [
    "<a id=\"t3.5.\"></a>\n",
    "## 3.5. 是否独自成行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_cell_guid": "67017a88-93d4-412b-9adf-8b4d1d9b9db0",
    "_uuid": "e0c3dc16292ef0bcabf0fc680d821ef654084ab4"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot('TravelAlone', 'survived', data=final, color=\"mediumturquoise\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "e9e68cef-5e74-46aa-8343-39afbbf00efe",
    "_uuid": "f160bd7399e024ae669d55f09caf6e7902768851"
   },
   "source": [
    "独自成行的乘客生还率比较低. 当时的年代, 大多数独自成行的乘客为男性居多."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "201b4c9d-b9f0-4ae9-8580-0b4e24ee62be",
    "_uuid": "693c25c25f3590f0b027725471ddd74d56f154af"
   },
   "source": [
    "<a id=\"t3.6.\"></a>\n",
    "## 3.6. 性别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_cell_guid": "7b416e59-8616-4a44-93e1-a8005eff78a9",
    "_uuid": "354794315925dff1e96229cc737eaf299aaea17a"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.barplot('sex', 'survived', data=df, color=\"aquamarine\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "490ed298-f0e4-466b-acc8-81280315e6a2",
    "_uuid": "80c02b9fe2151c443f189cbe44c9cacf7e5c44a4"
   },
   "source": [
    "很明显, 女性的生还率比较高"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "c833cbf5-74db-44ff-90fa-b600ff0a09d7",
    "_uuid": "39dbc095f99dcec6d25a7a4561e81bb641078622"
   },
   "source": [
    "<a id=\"t4.\"></a>\n",
    "# 4. 使用Logistic Regression做预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "b894002e-07cf-4d02-b708-a2ac387eed54",
    "_uuid": "e35125f8aa230d4875541aa4f6b5964d2f14a6a3"
   },
   "source": [
    "\n",
    "### 将数据集分为训练和测试数据集用于检测模型效果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_cell_guid": "84233f59-f3c7-4ea0-884d-96f8ad4d5b10",
    "_uuid": "46336228eeb864bc82e6739768122579d1c9634c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train/Test split results:\n",
      "准确率为 0.836\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/anaconda2/envs/py3/lib/python3.7/site-packages/sklearn/linear_model/logistic.py:432: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "# 使用如下特征做预测\n",
    "cols = [\"age\",\"fare\",\"TravelAlone\",\"pclass\",\"embarked_C\",\"embarked_S\",\"sex_male\"] \n",
    "\n",
    "# 创建 X (特征) 和 y (类别标签)\n",
    "X = final[cols]\n",
    "y = final['survived']\n",
    "\n",
    "# 将 X 和 y 分为两个部分\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=2)\n",
    "\n",
    "# 检测 logistic regression 模型的性能\n",
    "# TODO 添加代码:\n",
    "# 1.训练模型,  \n",
    "# 2.根据模型, 以 X_test 为输入, 生成变量 y_pred\n",
    "\n",
    "logreg = LogisticRegression()\n",
    "logreg.fit(X_train, y_train.values.reshape(-1))\n",
    "y_pred = logreg.predict(X_test)\n",
    "# print('在测试数据集上面的预测准确率: {:.2f}'.format(logreg.score(X_test, y_test)))\n",
    "\n",
    "\n",
    "print('Train/Test split results:')\n",
    "print(\"准确率为 %2.3f\" % accuracy_score(y_test, y_pred))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       0.88      0.87      0.88       175\n",
      "         1.0       0.75      0.76      0.75        87\n",
      "\n",
      "    accuracy                           0.84       262\n",
      "   macro avg       0.81      0.82      0.82       262\n",
      "weighted avg       0.84      0.84      0.84       262\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report\n",
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import roc_curve\n",
    "logit_roc_auc = roc_auc_score(y_test, logreg.predict(X_test))\n",
    "fpr, tpr, thresholds = roc_curve(y_test, logreg.predict_proba(X_test)[:,1])\n",
    "plt.figure()\n",
    "plt.plot(fpr, tpr, label='Logistic Regression (area = %0.2f)' % logit_roc_auc)\n",
    "plt.plot([0, 1], [0, 1],'r--')\n",
    "plt.xlim([0.0, 1.0])\n",
    "plt.ylim([0.0, 1.05])\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('Receiver operating characteristic')\n",
    "plt.legend(loc=\"lower right\")\n",
    "plt.savefig('Log_ROC')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
